{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scienceplots\n",
    "\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from tqdm import tqdm\n",
    "from torchinfo import summary\n",
    "from models.unet import UNet\n",
    "from models.dataset import InpaintingDataset\n",
    "from models.network import make_beta_schedule\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/guyi/miniconda3/envs/llama/lib/python3.10/site-packages/torchinfo/torchinfo.py:477: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  action_fn=lambda data: sys.getsizeof(data.storage()),\n",
      "/home/guyi/miniconda3/envs/llama/lib/python3.10/site-packages/torch/storage.py:665: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
      "  return super().__sizeof__() + self.nbytes()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "===============================================================================================\n",
       "Layer (type:depth-idx)                        Output Shape              Param #\n",
       "===============================================================================================\n",
       "UNet                                          [3, 3, 256, 256]          --\n",
       "├─Sequential: 1-1                             [3, 256]                  --\n",
       "│    └─Linear: 2-1                            [3, 256]                  16,640\n",
       "│    └─SiLU: 2-2                              [3, 256]                  --\n",
       "│    └─Linear: 2-3                            [3, 256]                  65,792\n",
       "├─ModuleList: 1-2                             --                        --\n",
       "│    └─EmbedSequential: 2-4                   [3, 64, 256, 256]         --\n",
       "│    │    └─Conv2d: 3-1                       [3, 64, 256, 256]         3,520\n",
       "│    └─EmbedSequential: 2-5                   [3, 64, 256, 256]         --\n",
       "│    │    └─ResBlock: 3-2                     [3, 64, 256, 256]         107,008\n",
       "│    └─EmbedSequential: 2-6                   [3, 64, 256, 256]         --\n",
       "│    │    └─ResBlock: 3-3                     [3, 64, 256, 256]         107,008\n",
       "│    └─EmbedSequential: 2-7                   [3, 64, 128, 128]         --\n",
       "│    │    └─ResBlock: 3-4                     [3, 64, 128, 128]         107,008\n",
       "│    └─EmbedSequential: 2-8                   [3, 128, 128, 128]        --\n",
       "│    │    └─ResBlock: 3-5                     [3, 128, 128, 128]        295,936\n",
       "│    └─EmbedSequential: 2-9                   [3, 128, 128, 128]        --\n",
       "│    │    └─ResBlock: 3-6                     [3, 128, 128, 128]        361,472\n",
       "│    └─EmbedSequential: 2-10                  [3, 128, 64, 64]          --\n",
       "│    │    └─ResBlock: 3-7                     [3, 128, 64, 64]          361,472\n",
       "│    └─EmbedSequential: 2-11                  [3, 256, 64, 64]          --\n",
       "│    │    └─ResBlock: 3-8                     [3, 256, 64, 64]          1,050,624\n",
       "│    └─EmbedSequential: 2-12                  [3, 256, 64, 64]          --\n",
       "│    │    └─ResBlock: 3-9                     [3, 256, 64, 64]          1,312,768\n",
       "│    └─EmbedSequential: 2-13                  [3, 256, 32, 32]          --\n",
       "│    │    └─ResBlock: 3-10                    [3, 256, 32, 32]          1,312,768\n",
       "│    └─EmbedSequential: 2-14                  [3, 512, 32, 32]          --\n",
       "│    │    └─ResBlock: 3-11                    [3, 512, 32, 32]          3,936,256\n",
       "│    └─EmbedSequential: 2-15                  [3, 512, 32, 32]          --\n",
       "│    │    └─ResBlock: 3-12                    [3, 512, 32, 32]          4,984,832\n",
       "├─EmbedSequential: 1-3                        [3, 512, 32, 32]          --\n",
       "│    └─ResBlock: 2-16                         [3, 512, 32, 32]          --\n",
       "│    │    └─Sequential: 3-13                  [3, 512, 32, 32]          2,360,832\n",
       "│    │    └─Sequential: 3-14                  [3, 1024]                 263,168\n",
       "│    │    └─Sequential: 3-15                  --                        2,360,832\n",
       "│    │    └─Identity: 3-16                    [3, 512, 32, 32]          --\n",
       "│    └─AttentionBlock: 2-17                   [3, 512, 32, 32]          --\n",
       "│    │    └─GroupNorm32: 3-17                 [3, 512, 1024]            1,024\n",
       "│    │    └─Conv1d: 3-18                      [3, 1536, 1024]           787,968\n",
       "│    │    └─QKVAttentionLegacy: 3-19          [3, 512, 1024]            --\n",
       "│    │    └─Conv1d: 3-20                      [3, 512, 1024]            262,656\n",
       "│    └─ResBlock: 2-18                         [3, 512, 32, 32]          --\n",
       "│    │    └─Sequential: 3-21                  [3, 512, 32, 32]          2,360,832\n",
       "│    │    └─Sequential: 3-22                  [3, 1024]                 263,168\n",
       "│    │    └─Sequential: 3-23                  --                        2,360,832\n",
       "│    │    └─Identity: 3-24                    [3, 512, 32, 32]          --\n",
       "├─ModuleList: 1-4                             --                        --\n",
       "│    └─EmbedSequential: 2-19                  [3, 512, 32, 32]          --\n",
       "│    │    └─ResBlock: 3-25                    [3, 512, 32, 32]          7,869,952\n",
       "│    └─EmbedSequential: 2-20                  [3, 512, 32, 32]          --\n",
       "│    │    └─ResBlock: 3-26                    [3, 512, 32, 32]          7,869,952\n",
       "│    └─EmbedSequential: 2-21                  [3, 512, 64, 64]          --\n",
       "│    │    └─ResBlock: 3-27                    [3, 512, 32, 32]          6,558,720\n",
       "│    │    └─ResBlock: 3-28                    [3, 512, 64, 64]          4,984,832\n",
       "│    └─EmbedSequential: 2-22                  [3, 256, 64, 64]          --\n",
       "│    │    └─ResBlock: 3-29                    [3, 256, 64, 64]          2,690,304\n",
       "│    └─EmbedSequential: 2-23                  [3, 256, 64, 64]          --\n",
       "│    │    └─ResBlock: 3-30                    [3, 256, 64, 64]          2,034,432\n",
       "│    └─EmbedSequential: 2-24                  [3, 256, 128, 128]        --\n",
       "│    │    └─ResBlock: 3-31                    [3, 256, 64, 64]          1,706,496\n",
       "│    │    └─ResBlock: 3-32                    [3, 256, 128, 128]        1,312,768\n",
       "│    └─EmbedSequential: 2-25                  [3, 128, 128, 128]        --\n",
       "│    │    └─ResBlock: 3-33                    [3, 128, 128, 128]        706,176\n",
       "│    └─EmbedSequential: 2-26                  [3, 128, 128, 128]        --\n",
       "│    │    └─ResBlock: 3-34                    [3, 128, 128, 128]        542,080\n",
       "│    └─EmbedSequential: 2-27                  [3, 128, 256, 256]        --\n",
       "│    │    └─ResBlock: 3-35                    [3, 128, 128, 128]        460,032\n",
       "│    │    └─ResBlock: 3-36                    [3, 128, 256, 256]        361,472\n",
       "│    └─EmbedSequential: 2-28                  [3, 64, 256, 256]         --\n",
       "│    │    └─ResBlock: 3-37                    [3, 64, 256, 256]         193,344\n",
       "│    └─EmbedSequential: 2-29                  [3, 64, 256, 256]         --\n",
       "│    │    └─ResBlock: 3-38                    [3, 64, 256, 256]         152,256\n",
       "│    └─EmbedSequential: 2-30                  [3, 64, 256, 256]         --\n",
       "│    │    └─ResBlock: 3-39                    [3, 64, 256, 256]         152,256\n",
       "├─Sequential: 1-5                             [3, 3, 256, 256]          --\n",
       "│    └─GroupNorm32: 2-31                      [3, 64, 256, 256]         128\n",
       "│    └─SiLU: 2-32                             [3, 64, 256, 256]         --\n",
       "│    └─Conv2d: 2-33                           [3, 3, 256, 256]          1,731\n",
       "===============================================================================================\n",
       "Total params: 62,641,347\n",
       "Trainable params: 62,641,347\n",
       "Non-trainable params: 0\n",
       "Total mult-adds (G): 617.13\n",
       "===============================================================================================\n",
       "Input size (MB): 4.72\n",
       "Forward/backward pass size (MB): 6906.80\n",
       "Params size (MB): 250.57\n",
       "Estimated Total Size (MB): 7162.09\n",
       "==============================================================================================="
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b, c, h, w = 3, 3, 256, 256\n",
    "\n",
    "denoise_fn = UNet(\n",
    "    in_channel=2 * c,\n",
    "    out_channel=c,\n",
    "    inner_channel=64,\n",
    "    channel_mults=(1, 2, 4, 8),\n",
    "    attn_res=[16],\n",
    "    num_head_channels=32,\n",
    "    res_blocks=2,\n",
    "    dropout=0.2,\n",
    "    image_size=256,\n",
    ")\n",
    "\n",
    "x = torch.randn((b, 2 * c, h, w))\n",
    "emb = torch.ones((b, 1))\n",
    "summary(denoise_fn, input_data=(x, emb), device=torch.device(\"cpu\"))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = InpaintingDataset(data_path=\"./data/celeba_hq_256_full.flist\", mask_mode=\"hybrid\", data_len=-1, image_size=[256, 256])\n",
    "len(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'gt_image': tensor([[[-0.3098, -0.3098, -0.3098,  ..., -0.7569, -0.7569, -0.7569],\n",
       "          [-0.3098, -0.3098, -0.3098,  ..., -0.7647, -0.7569, -0.7569],\n",
       "          [-0.3098, -0.3098, -0.3098,  ..., -0.7647, -0.7647, -0.7647],\n",
       "          ...,\n",
       "          [-0.2784, -0.2784, -0.2784,  ...,  0.0196, -0.0431, -0.0980],\n",
       "          [-0.2784, -0.2784, -0.2784,  ...,  0.0510, -0.0039, -0.0588],\n",
       "          [-0.2863, -0.2863, -0.2863,  ...,  0.0745,  0.0196, -0.0353]],\n",
       " \n",
       "         [[-0.3961, -0.3961, -0.3961,  ..., -0.5843, -0.5843, -0.5843],\n",
       "          [-0.3961, -0.3961, -0.3961,  ..., -0.5922, -0.5843, -0.5843],\n",
       "          [-0.3961, -0.3961, -0.3961,  ..., -0.5922, -0.5922, -0.5922],\n",
       "          ...,\n",
       "          [-0.3490, -0.3490, -0.3490,  ...,  0.1137,  0.0588,  0.0196],\n",
       "          [-0.3490, -0.3490, -0.3490,  ...,  0.1451,  0.0980,  0.0588],\n",
       "          [-0.3569, -0.3569, -0.3569,  ...,  0.1686,  0.1216,  0.0824]],\n",
       " \n",
       "         [[-0.5686, -0.5686, -0.5686,  ..., -0.4039, -0.4039, -0.4039],\n",
       "          [-0.5686, -0.5686, -0.5686,  ..., -0.4118, -0.4039, -0.4039],\n",
       "          [-0.5686, -0.5686, -0.5686,  ..., -0.4118, -0.4118, -0.4118],\n",
       "          ...,\n",
       "          [-0.5765, -0.5765, -0.5765,  ...,  0.0353, -0.0118, -0.0353],\n",
       "          [-0.5765, -0.5765, -0.5765,  ...,  0.0667,  0.0275,  0.0039],\n",
       "          [-0.5843, -0.5843, -0.5843,  ...,  0.0902,  0.0510,  0.0275]]]),\n",
       " 'cond_image': tensor([[[-0.3098, -0.3098, -0.3098,  ..., -0.7569, -0.7569, -0.7569],\n",
       "          [-0.3098, -0.3098, -0.3098,  ..., -0.7647, -0.7569, -0.7569],\n",
       "          [-0.3098, -0.3098, -0.3098,  ..., -0.7647, -0.7647, -0.7647],\n",
       "          ...,\n",
       "          [-0.2784, -0.2784, -0.2784,  ...,  0.0196, -0.0431, -0.0980],\n",
       "          [-0.2784, -0.2784, -0.2784,  ...,  0.0510, -0.0039, -0.0588],\n",
       "          [-0.2863, -0.2863, -0.2863,  ...,  0.0745,  0.0196, -0.0353]],\n",
       " \n",
       "         [[-0.3961, -0.3961, -0.3961,  ..., -0.5843, -0.5843, -0.5843],\n",
       "          [-0.3961, -0.3961, -0.3961,  ..., -0.5922, -0.5843, -0.5843],\n",
       "          [-0.3961, -0.3961, -0.3961,  ..., -0.5922, -0.5922, -0.5922],\n",
       "          ...,\n",
       "          [-0.3490, -0.3490, -0.3490,  ...,  0.1137,  0.0588,  0.0196],\n",
       "          [-0.3490, -0.3490, -0.3490,  ...,  0.1451,  0.0980,  0.0588],\n",
       "          [-0.3569, -0.3569, -0.3569,  ...,  0.1686,  0.1216,  0.0824]],\n",
       " \n",
       "         [[-0.5686, -0.5686, -0.5686,  ..., -0.4039, -0.4039, -0.4039],\n",
       "          [-0.5686, -0.5686, -0.5686,  ..., -0.4118, -0.4039, -0.4039],\n",
       "          [-0.5686, -0.5686, -0.5686,  ..., -0.4118, -0.4118, -0.4118],\n",
       "          ...,\n",
       "          [-0.5765, -0.5765, -0.5765,  ...,  0.0353, -0.0118, -0.0353],\n",
       "          [-0.5765, -0.5765, -0.5765,  ...,  0.0667,  0.0275,  0.0039],\n",
       "          [-0.5843, -0.5843, -0.5843,  ...,  0.0902,  0.0510,  0.0275]]]),\n",
       " 'mask_image': tensor([[[-0.3098, -0.3098, -0.3098,  ..., -0.7569, -0.7569, -0.7569],\n",
       "          [-0.3098, -0.3098, -0.3098,  ..., -0.7647, -0.7569, -0.7569],\n",
       "          [-0.3098, -0.3098, -0.3098,  ..., -0.7647, -0.7647, -0.7647],\n",
       "          ...,\n",
       "          [-0.2784, -0.2784, -0.2784,  ...,  0.0196, -0.0431, -0.0980],\n",
       "          [-0.2784, -0.2784, -0.2784,  ...,  0.0510, -0.0039, -0.0588],\n",
       "          [-0.2863, -0.2863, -0.2863,  ...,  0.0745,  0.0196, -0.0353]],\n",
       " \n",
       "         [[-0.3961, -0.3961, -0.3961,  ..., -0.5843, -0.5843, -0.5843],\n",
       "          [-0.3961, -0.3961, -0.3961,  ..., -0.5922, -0.5843, -0.5843],\n",
       "          [-0.3961, -0.3961, -0.3961,  ..., -0.5922, -0.5922, -0.5922],\n",
       "          ...,\n",
       "          [-0.3490, -0.3490, -0.3490,  ...,  0.1137,  0.0588,  0.0196],\n",
       "          [-0.3490, -0.3490, -0.3490,  ...,  0.1451,  0.0980,  0.0588],\n",
       "          [-0.3569, -0.3569, -0.3569,  ...,  0.1686,  0.1216,  0.0824]],\n",
       " \n",
       "         [[-0.5686, -0.5686, -0.5686,  ..., -0.4039, -0.4039, -0.4039],\n",
       "          [-0.5686, -0.5686, -0.5686,  ..., -0.4118, -0.4039, -0.4039],\n",
       "          [-0.5686, -0.5686, -0.5686,  ..., -0.4118, -0.4118, -0.4118],\n",
       "          ...,\n",
       "          [-0.5765, -0.5765, -0.5765,  ...,  0.0353, -0.0118, -0.0353],\n",
       "          [-0.5765, -0.5765, -0.5765,  ...,  0.0667,  0.0275,  0.0039],\n",
       "          [-0.5843, -0.5843, -0.5843,  ...,  0.0902,  0.0510,  0.0275]]]),\n",
       " 'mask': tensor([[[0, 0, 0,  ..., 0, 0, 0],\n",
       "          [0, 0, 0,  ..., 0, 0, 0],\n",
       "          [0, 0, 0,  ..., 0, 0, 0],\n",
       "          ...,\n",
       "          [0, 0, 0,  ..., 0, 0, 0],\n",
       "          [0, 0, 0,  ..., 0, 0, 0],\n",
       "          [0, 0, 0,  ..., 0, 0, 0]]], dtype=torch.uint8),\n",
       " 'path': '00000.jpg'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fbd3813c070>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "single_data = dataset[0]\n",
    "plt.subplots(1, 4, figsize=(16, 4), dpi=100)\n",
    "plt.subplot(1, 4, 1)\n",
    "plt.imshow((single_data[\"gt_image\"].permute(1, 2, 0) + 1) / 2)\n",
    "plt.subplot(1, 4, 2)\n",
    "plt.imshow((single_data[\"cond_image\"].permute(1, 2, 0) + 1) / 2)\n",
    "plt.subplot(1, 4, 3)\n",
    "plt.imshow(single_data[\"mask\"].permute(1, 2, 0), cmap=\"gray\")\n",
    "plt.subplot(1, 4, 4)\n",
    "plt.imshow((single_data[\"mask_image\"].permute(1, 2, 0) + 1) / 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.0000000e-06, 6.0020010e-06, 1.1004002e-05, ..., 9.9899960e-03,\n",
       "       9.9949980e-03, 1.0000000e-02])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "betas = make_beta_schedule(\"linear\", 2000, 1e-6, 0.01)\n",
    "betas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-0.002,  0.   ,  0.002,  0.004,  0.006,  0.008,  0.01 ,  0.012]),\n",
       " [Text(0, -0.002, '$\\\\mathdefault{−0.002}$'),\n",
       "  Text(0, 0.0, '$\\\\mathdefault{0.000}$'),\n",
       "  Text(0, 0.002, '$\\\\mathdefault{0.002}$'),\n",
       "  Text(0, 0.004, '$\\\\mathdefault{0.004}$'),\n",
       "  Text(0, 0.006, '$\\\\mathdefault{0.006}$'),\n",
       "  Text(0, 0.008, '$\\\\mathdefault{0.008}$'),\n",
       "  Text(0, 0.01, '$\\\\mathdefault{0.010}$'),\n",
       "  Text(0, 0.012, '$\\\\mathdefault{0.012}$')])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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L7gvESQ3M5mQ2HxAnVajodOXKFV5++WW6d+/OtWvX2L59O6+99ppSCQkY308yUiIIgiAINSArK4vJkyeTm5vLb3/7W55//nnq1atndFhKIo8Eu4EvHgkuLi7GYrHoWqfRiJMamM3JbD4gTqpw6tQp3nnnHf74xz8SGRnJvHnz6Nq1q9Fh1Qhf9JM8EqwAe/fuNToE3REnNTCbk9l8QJxUYNeuXdx///3MmjWLF198kR07diifkIDx/SRJiUGcOHHC6BB0R5zUwGxOZvMBcQpkLl++zHPPPed6tNdut/Piiy+a5naN0f0kc0oMwuhNl3yBOKmB2ZzM5gPiFKhs376dyZMnc+TIEX7/+98THh5Op06djA5LV4zuJ5lT4ga+mFNSVlZG7dq1da3TaMRJDczmZDYfEKdA49KlS7z00kv8+c9/xmq1Mm/ePCIjI5V2qgpfOMmcEgVYuHCh0SHojjipgdmczOYD4hRIZGZm0qVLF9566y1ee+01tm3b5lpgTFWn6jDaSZISQRAEQfgBFy9e5KmnnqJPnz40b96c3bt38+yzz1Knjsx68CXy7hpEhw4djA5Bd8RJDczmZDYfECej+eqrr5gyZQrHjh1j9uzZPPnkk5Xe0lDJyV2MdpKREoMIDg42OgTdESc1MJuT2XxAnIziwoUL/PrXv+aBBx6gVatW7N27l6effrrKORYqOHmK0U6SlLiBL3YJzszM1K2uQEGc1MBsTmbzAXEygi+//JJOnTrx3nvv8ec//5nNmzdz7733Vlsm0J28QU8nb3YJlqTEDZy7BCcmJhodiiAIgqAjJSUlzJgxg/79+3P77bezd+/eKm/XCJ6RmJjI8uXLCQkJcbuMzCkxiJiYGKND0B1xUgOzOZnNB8TJX2zYsIFHHnmE06dP89ZbbzFjxgxq1XL/b/VAdKopRjvJSIlBHDhwwOgQdEec1MBsTmbzAXHyNefOnWP69OkMGjSIO++8k3379vH44497lJBAYDnphdFOMlJiEMeOHTM6BN0RJzUwm5PZfECcfMm6deuYOnUqBQUF/P3vf2f69OkeJyNOAsVJT4x2kpESg2jQoIHRIeiOOKmB2ZzM5gPi5AvOnj3LtGnTGDx4MD/5yU/Yv38/jz32mNcJCRjv5AuMdpJl5t3AF8vMC4IgCP5hzZo1TJs2jeLiYt58802SkpIICgoyOqybBt2XmU9PTycmJoaYmBiioqKIiYkhIyOjRkHWpF5vy82ePdtVJiwsjKioKObOnVtjD2+YP3++Ie36EnFSA7M5mc0HxEkviouLmTJlCsOGDeOnP/0p2dnZJCcn65aQSD/pzw3nlMTHx5Oeno7dbsdqtQL/lxSkpKQwa9Ysrxr2tl5vyuXl5REVFUV0dDQ2mw2LxQJARkYGMTEx2Gw21q1b55WHt5hxgEqc1MBsTmbzAXHSg88//5ykpCRKSkp47733eOSRR3QfHZF+8k0AVZKUlKQBWmpqapWv2Wy26qrQtV5vy4WGhmoWi6XSWGbNmlVlnU5GjRpVnY5X7Ny5U/c6jUac1MBsTmbz0TRxqgmFhYXaww8/rAHa0KFDtaNHj/qsLekn9/DkGlplUuJwODRAqypvsdvtGqCFhoZ6FJy39da0nNVqrbTcunXrNECLi4urMmZfJCXHjh3TvU6jESc1MJuT2Xw0TZy8ZdmyZVqbNm20Zs2aafPmzdPKy8t92p70k3t4cg2tck6J8zZIdHR0pa9brVYsFgt5eXkezS/xtl5vy+Xl5QGQlZVFcXHxj8o5f+fv9f6/+uorv7bnD8RJDczmZDYfECdPKSgo4KGHHmL06NFERUWRk5PDpEmTfD6ZVfpJf6pMSpwXdue8jcoIDQ0FIDU11e0Gva3X23LdunVz/XvatGk/KuM8Njk5+YaxC4IgCIHF4sWLiYiIYNWqVXz00UesWLHCo2XNhcCi0qSkuLjYNcLQokWLKgs7k4CsrCy3GvO23prEY7FYXKMszgmxztGR2bNnk5GRgc1mqzbZ8QUDBgzwa3v+QJzUwGxOZvMBcXKH//3vf0ycOJHx48dz3333kZOTw89//nO/Puor/aQ/lT5940wAANeTKpXhvOVR8fjq8LbemsaTkpJCQUGBKwm5++676datGxaLBYfD4UpmquLIkSNERUUBcNtttxEeHk7Xrl1p1aoVPXr0YOXKlcD1UZzy8nL27NkDwOjRo9myZQsFBQUEBwfTr18/li5dClyf4dytWzfsdjsAI0aMYNeuXZw6dYqmTZsyePBg0tPTAYiIiKBJkyZs374dgCFDhpCdnU1+fj6NGzdm5MiRLFiwAIB7772Xli1bsnXrVuD67a5Dhw5x9OhR6tevz7hx41iwYAHl5eWEhYUREhLC5s2bAejfvz9Hjx4lLy+P2rVrM2HCBBYtWsSVK1e48847CQsLY8OGDQD06dOH06dPc+jQIeD6xksffvgh7dq144477iA8PJy1a9cC0LNnT86ePUtubi5w/QmqNWvWcP78edq0aYPVamXVqlUAdO/encuXL7N//34Axo4dy6ZNmygqKqJly5b07NmTFStWANC1a1cAdu/eDcCoUaPYtm0bZ86coXnz5vTv358lS5YA0LFjRxo0aMDOnTsBGD58OFlZWZw8eZJbbrmFoUOHYrPZAAgPD6dZs2Zs27aNw4cP88tf/pLc3FyOHz9Ow4YNGTNmjGvH6Pbt29OqVSu2bNkCwMCBA3E4HHz77bfUq1eP8ePHs3DhQsrKyggNDaVdu3Zs2rQJgH79+pGfn4/D4aBWrVokJCSwePFiSktLadeuHe3bt3eNEPbu3ZszZ85w8OBBABISEli5ciUXLlwgJCSEyMhIvvjiCwDuu+8+SkpKyMnJASAuLo61a9dy7tw5WrdujaZpbNy4EYCoqCiuXr3Kvn37ABgzZgybN2+msLCQFi1a0KdPH5YtWwZAly5dqFWrlivpHzlyJDt27OD06dM0a9aMQYMGsXjxYgAiIyNp1KgRO3bsAGDYsGHs3buXEydO0KRJE4YPH87ChQsB6NChA8HBwa7dSWNiYjhw4ADHjh2jQYMGjB07lvnz56NpGvfccw9t2rRxDTEPGDCAlStX0rhxY+rUqUN8fDw2m41r165x9913c9ddd7lc+/bty8mTJzl8+DBBQUFMnDiRJUuWcPnyZdq2bUuHDh1cT+H16tWLwsJC15LbEyZMYNWqVZSUlHD77bfTuXNnVq9eDUCPHj24ePEi2dnZAIwbN47169dz9uxZr78jDh8+zPjx46lbt65pviPuvvtuvv76ay5dulTj74hly5bxhz/8gfLycubNm0fr1q3ZtGmT378jPvroI+655x4GDx5smu+IgoICHn74YT7//HOvvyPmzJlDZmYmrVu3pri4mNLSUtymsokmzsmf3ODpmpSUFNdxRUVFN5zA4m29esXjfNLG+VPd5NaK+GKi62effaZ7nUYjTmpgNiez+WiaOFXFqVOntLi4OA3Qxo0bp508eVKHyLxH+sk9dJnoakYKCgpcE2Lh+u2csLAwt0d69KROHfNtOyROamA2J7P5gDj9EE3TmD9/PuHh4WzatIkFCxaQnp5O69atdYzQc6Sf9KfSpKTikyiFhYVVFq74NEt1t1VqWq8e8URFRZGVlYXdbqeoqIi4uDjg/xZW83diEh8f79f2/IE4qYHZnMzmA+JUkZMnTzJ+/HgSExMZNGgQubm5TJgwISCWiZd+0p9Kk5KKcywqe4zWiTNBcCchqUm9NY0nJiaGrKws1/1AAJvN5nrypri42O9P31SMxSyIkxqYzclsPiBOcH105NNPPyUiIoItW7Zgs9lYsGABt956q48i9BzpJ/2pNCmxWCyuRKCgoKDKws4Eoaq1Q/SqtybxZGRkkJGR8b3bNk6SkpJciUlGRoZfR0uuXbvmt7b8hTipgdmczOYD4nTixAlGjx7NQw89xJAhQ8jNzXWNbgcSN3s/+YIq55Q4PwDVPe67a9cu4PpIhLt4W6+35ZyJRlVP2CQlJf3oWH9w9913+60tfyFOamA2J7P5wM3rpGkaH374IREREezYsYMlS5bw2Wef0bJlSz9E6Dk3az/5lKpmwN5oWXfn61XtKaN3vd6Wcz65U9Uy85p2fW8cqnmCyBdP33z33Xe612k04qQGZnMym4+m3ZxOx48f14YPH64B2kMPPaQVFBT4KTLvuRn7yRt0efomNDTUNYrgfBa+Is4FyarblbeyeRre1uttuejoaCwWC1lZWZWOsjgXZqvs9o4vca6dYCbESQ3M5mQ2H7i5nDRN41//+hcRERHs3r2b5cuX8/HHH/t96w9vuJn6yV9U+0hwamoqcXFxTJs27Xu3NjIyMpg7dy4pKSnfu/1RsVxWVhZz586tNBGoSb3elFu/fj1wfVZxxXiKi4uJj4/HYrG4jhEEQRD8w7Fjxxg2bBiPPPIIY8eOJScnh1GjRhkdlmAgN3wg2WazMXfuXJKTk7FYLK7JpHa7vcql2RMSEkhPTyc0NLTKY7yp19tyVquVoqIi3njjDaZNm0ZxcbFrjklMTIxrFUd/0rdvX7+36WvESQ3M5mQ2HzC/k6ZpvP/++/zmN7+hadOmfP755wwfPtzA6LzD7P1kCLrfPDIhvphTsnPnTt3rNBpxUgOzOZnNR9PM7fTf//5Xi46O1gDtkUce0YqLiw2OzHvM3E96Iiu6KsDhw4eNDkF3xEkNzOZkNh8wp9PBgwd59913iYyM5ODBg6xZs4b333+fZs2aGR2a15ixn4x2Mt8auYoQCKsR6o04qYHZnMzmA+Zz+uabb3j99dfJzc0lKSmJP/3pTzRt2tTosGqM2foJjHcK0jRNMzQCBYiKiiIkJITExEQSExONDkcQBEEJysvL+cc//sGzzz5Ly5Ytef/9991ebFNQn7S0NNLS0sjPz3ftdn0j5PaNG4SEhLB8+XJdExLndtlmQpzUwGxOZvMBczg5HA4GDBjAr371K37xi1/w+uuvmy4hMUM//RA9nRITE1m+fDkhISFul5GkxCAuX75sdAi6I05qYDYns/mA2k7l5eW89dZbdOzYkWPHjrFhwwb+/ve/G35bwBeo3E9VYbSTJCUG0bZtW6ND0B1xUgOzOZnNB9R1OnToEP369eOJJ55g6tSp7Nu3jwEDBgDqOlWHOOmPJCUG0aFDB6ND0B1xUgOzOZnNB9RzKisr489//jOdO3fm5MmTfPnll7z99ts0adLEdYxqTu4gTvojSYlBGLFgm68RJzUwm5PZfEAtpwMHDtCnTx+eeeYZpk+fzr59++jXr9+PjlPJyV3ESX8kKREEQRA85tq1a8yePZsuXbpQUFDAV199xV//+lcaNWpkdGiCwkhSYhC9evUyOgTdESc1MJuT2Xwg8J1yc3Pp3bs3zz77LDNmzGDv3r307t272jKB7uQN4qQ/kpQYRGFhodEh6I44qYHZnMzmA4HrdO3aNd544w26du3KuXPn2Lp1K2+++SYNGza8YdlAdaoJ4qQ/kpQYxIEDB4wOQXfESQ3M5mQ2HwhMp/3793P//ffzwgsv8OSTT7J792569uzpdvlAdKop4qQ/kpQIgiAIVXL16lVeeeUVoqKiuHTpEtu2beOPf/wjDRo0MDo0wYTIMvNuEBsby/Lly3Wts6ysjNq1a+tap9GIkxqYzclsPhA4Tnv37mXy5Mns27ePmTNn8uKLL1K/fn2v6goUJz0RJ/fw5BoqIyUGsWrVKqND0B1xUgOzOZnNB4x3unLlCi+//DLdunXj6tWrbN++nddee83rhASMd/IF4qQ/skuwQZSUlBgdgu6IkxqYzclsPmCs0+7du5k0aRK5ubk899xzPP/88zVKRpxIP6mB0U4yUuIG+fn5xMbGkpaWpludt99+u251BQripAZmczKbDxjjVFpayu9+9zu6d+9OUFAQO3bs4A9/+IMuCQlIP6mCnk5paWnExsaSn5/vdhmZU+IGvphTUlxcjMVi0bVOoxEnNTCbk9l8wP9Ou3btYvLkyRw4cIAXXniB5557jnr16unahvSTGvjCSeaUKMDq1auNDkF3xEkNzOZkNh/wn9Ply5f57W9/y/3330/dunXZtWsXL730ku4JCUg/qYLRTjKnRBAE4SZk+/btTJ48mSNHjvDyyy8zc+ZM6tata3RYwk2OjJQYRI8ePYwOQXfESQ3M5mQ2H/Ct06VLl0hJSaFXr140atSIrKwsXnjhBZ8nJNJPamC0k4yUGMTFixeNDkF3xEkNzOZkNh/wndO2bduYPHky33zzDa+99hpPP/00der45zIg/aQGRjvJSIlBZGdnGx2C7oiTGpjNyWw+oL/TxYsX+c1vfkPv3r1p1qwZu3fv5tlnn/VbQgLST6pgtJOMlAiCIJiYLVu2MGXKFI4ePcqsWbN48skn/ZqMCIInyCPBbuCLR4JLS0t1e/4/UBAnNTCbk9l8QB+nCxcu8Pzzz/P2229z//33869//YsOHTroFKHnSD+pgS+c5JFgBVi/fr3RIeiOOKmB2ZzM5gM1d/ryyy/p1KkTqampvPnmm3z11VeGJiQg/aQKRjtJUmIQZ8+eNToE3REnNTCbk9l8wHunkpISZsyYQf/+/bn99tvZt28fTz31VEBsGif9pAZGO8mNRYNo1aqV0SHojjipgdmczOYD3jlt2LCBRx55hFOnTvHWW28xY8YMatUKnL87pZ/UwGinwPnE3mQY/Sy4LxAnNTCbk9l8wDOn8+fP8+ijjzJo0CDuvPNO9u/fz+OPPx5QCQlIP6mC0U6B9am9iVi5cqXRIeiOOKmB2ZzM5gPuO2VkZBAZGcnHH3/MnDlz2LBhA2FhYT6Ozjtu5n5SCaOdJClxA1/sEiwIguAtZ8+eJSkpiZiYGMLCwti/fz+//OUvA250RLi58WaXYJlT4gYhISG6PxJstVp1rS8QECc1MJuT2Xygeqc1a9Ywbdo0iouLeffdd0lKSlIiGbnZ+klV9HRKTEwkMTGR2NhYt8sE/ifZpJSXlxsdgu6IkxqYzclsPlC5U3FxMVOmTGHYsGF06NCB7Oxspk+frkRCAjdPP6mO0U5qfJpNyJ49e4wOQXfESQ3M5mQ2H/ix0+eff05ERATp6em89957rF27ljvvvNOY4LzkZugnM2C0kyQlgiAIAUpRURG/+MUvGDlyJJ06dSInJ4epU6cSFBRkdGiC4BNkmXmuD4vm5eURGhqKxWL50eu+WGb+4sWLNGrUSNc6jUac1MBsTmbzgetOGRkZTJ8+nYsXL/LXv/6VSZMmKZ2MmLWfxOnGyDLzbpCVlUV8fDxhYWHEx8eza9cuv7a/ZcsWv7bnD8RJDczmZDafgoICRo0axejRo7FareTk5DB58mSlExIwXz+BOPkCt56+SU9PJzU1FYDCwkKCg4OZOXMm0dHRNWrc23prEk9eXh7JyclkZGQQFxeH3W6vdHTE1xQUFPi9TV8jTmpgNicz+SxZsoRHH32U8+fP8+GHH/Lzn/9c+WTEiZn6yYk4+QDtBsTFxWmAZrfbXb+z2WwaoKWkpNyouO711iQeu92uWSwWDdBsNpvbsY4aNcrtY91lzZo1utdpNOKkBmZzMoPP6dOntYSEBA3QYmNjtU8//dTokHTHDP30Q8TJPTy5hlablCQlJWmAlpqaWuVrnlzca1pvTeJZt26dBngVsy+SkosXL+pep9GIkxqYzUl1H5vNpt16661acHCw9umnn2rl5eXKO1WGOKmBL5x0SUocDofrIl4ZdrtdA7TQ0FCPgvO23prEU1RU5BohSUpK8iheTfNNUvLZZ5/pXqfRiJMamM1JVZ9Tp05p8fHxGqCNHTtW++6771yvqepUHeKkBr5w8uQaWuVE11mzZgFUOU/DarVisVjIy8sjIyPD7dtF3tZbk3icqx9WrEcQBMEINE1jwYIFREREsHHjRubPn8+iRYto06aN0aEJguFUmZQ4L+zVLTkbGhoK4Jp06g7e1uttuby8PNLT04HrCY3FYqG4uJisrCxXomIEnTp1MqxtXyFOamA2J5V8Tp48yfjx45k4cSIDBgwgJyeHhISEH01mVcnJXcRJDYx2qjQpca7bAdCiRYsqCzuTgKysLLca87bemsRTMUGxWq3ExMTQvHlzoqKiaN68Oc2bN2fu3Lluxa8ndevW9Xubvkac1MBsTir4aJrGp59+SkREBFu2bMFms7Fw4UJatWpV6fEqOHmKOKmB0U6VPhLsTACAah+XDQ4O/tHx1eFtvTWJp+KtnLy8PFJTUwkNDSUrK4vU1FTmzp1LcnIydru9yhGfI0eOEBUVBcBtt91GeHg4Xbt2pVWrVvTo0cO11bPVaqW8vNy1TO/o0aPZsmULBQUFBAcH069fP5YuXQrA8ePHGT16NHa7HYARI0awa9cuTp06RdOmTRk8eLBrhCciIoImTZqwfft2AIYMGUJ2djb5+fk0btyYkSNHsmDBAgDuvfdeWrZsydatW4Hro0OHDh3i6NGj1K9fn3HjxrFgwQLKy8sJCwsjJCSEzZs3A9C/f3+OHj1KXl4etWvXZsKECSxatIgrV65w5513EhYWxoYNGwDo06cPp0+f5tChQ8D1jZf++c9/0r17d+644w7Cw8NZu3YtAD179uTs2bPk5uYCEB8fz5o1azh//jxt2rTBarWyatUqALp3787ly5fZv38/AGPHjmXTpk0UFRXRsmVLevbsyYoVKwDo2rUrALt37wZg1KhRbNu2jTNnztC8eXP69+/PkiVLAOjYsSMNGjRg586dAAwfPpysrCxOnjzJLbfcwtChQ7HZbACEh4fTrFkztm3bRmZmJi+//DK5ubkcP36chg0bMmbMGNeO0e3bt6dVq1auZ/sHDhyIw+Hg22+/pV69eowfP56FCxdSVlZGaGgo7dq1Y9OmTQD069eP/Px8HA4HtWrVIiEhgcWLF1NaWkq7du1o37696/Pbu3dvzpw5w8GDBwFISEhg5cqVXLhwgZCQECIjI/niiy8AuO+++ygpKSEnJweAuLg41q5dy7lz52jdujXffPON63MXFRXF1atX2bdvHwBjxoxh8+bNFBYW0qJFC/r06cOyZcsA6NKlC7Vq1XIl/SNHjmTHjh2cPn2aZs2aMWjQIBYvXgxAZGQkjRo1YseOHQAMGzaMvXv3cuLECZo0acLw4cNZuHAhAB06dCA4OJjMzEwAYmJiOHDgAMeOHaNBgwaMHTuW+fPno2ka99xzD23atOGrr74CYMCAAcyfP5977rmHOnXqEB8fj81m49q1a9x9993cddddbNy4EYC+ffty8uRJDh8+TFBQEBMnTmTJkiVcvnyZtm3b0qFDB9atWwdAr169KCws5MCBAwBMmDCBVatWUVJSwu23307nzp1ZvXo1AD169ODixYtkZ2cDMG7cONavX8/Zs2dp1aoV7dq1IzExEbvdzvDhw3n++ef59ttvSUtLq/I7IjMzk+nTp1O3bl3TfEcA5ObmcunSJdN8R7zzzjv06tWLwYMHm+Y7Iicnh5SUFD7//HOvvyPmzJlDZmYmrVu3pri4mNLSUtymsokm7j6pkpKS4jquqKjohhNYvK23JvE4J7hardZKyzif2uEHjxlXRCa6uoc4qYHZnALVp7y8XPvwww81i8WitW7dWlu8eLHbZQPVqSaIkxoE7ERXs+CcN9KtW7dKX6848dWTuTE1ZcSIEX5ry1+IkxqYzSkQffLz8xk1apRr35qcnBzGjh3rdvlAdKop4qQGRjtVmpQ4b4PA9RVTq6LiRFF3VkX1tl494qkqPovF4po868+l5v29rL0/ECc1MJtTIPlomsa8efOIiIggKyuLZcuW8fHHH1c7F64yAslJL8RJDYx2qjQpcU4YBap9QsWZILi7TLu39dYkHue/qyvnrN/duTF6cOrUKb+15S/ESQ3M5hQoPseOHWPYsGFMmTKFMWPGkJOTQ2xsrFd1BYqTnoiTGhjtVGlSYrFYXBfq6tbBd17o3d0Dx9t6axKP87ZNdQmHcySmYvLja5o2beq3tvyFOKmB2ZyM9tE0jffee4+IiAiys7NZuXIl//73v2nevLnXdRrt5AvESQ2MdqpyTklcXBxQ/eO+zmGemJgYtxv0tl5vyzn/XV1S4hxhqWreiS8YPHiw39ryF+KkBmZzMtLn22+/ZciQISQlJREfH092drYu9+TN1kcgTqpguFNVM2BvtKy783WLxeLRLFxv6/W2XFFR0Q2frrFarRqgrVu3rtLX5ekb9xAnNTCbkxE+ZWVl2rvvvqs1adJEu+OOO7TVq1frWr/Z+kjTxEkVAvbpm9DQUJKSkgBcz8JXxPnUSmXLtufl5REVFUVycrJu9XpbzmKxkJKSAsAbb7xRaaxZWVlER0e7fRtKEISbl2+++YaYmBgeffRRHnzwQXJychg6dKjRYQmCObhR1hIXF6dZLBbN4XC4fudcNyQlJaXSMhXXC6lqdMKbemtSzrkeScUN+YqKijSr1apZrdZq11nxxUjJ3r17da/TaMRJDczm5C+fsrIybc6cOVrjxo21du3aaWvXrvVZW2brI00TJ1XwhZMn19BKV3StiM1mc6166tw3BsBut1e5D01CQgLp6emEhoZWeYw39dakXGpqKjExMSxYsICwsDDXo8YJCQmukRR/0qRJE7+36WvESQ3M5uQPH4fDwSOPPMKXX37Jo48+yqxZs7jlllt81p7Z+gjESRUMd9I9JTIhMqfEPcRJDczm5EufsrIy7W9/+5vWqFEj7e6779bWr1/vs7YqYrY+0jRxUoWAnVMiCIJwM3P48GEeeOABnnjiCaZMmcK+ffsYOHCg0WEJgqmRpMQghgwZYnQIuiNOamA2J719ysrK+Mtf/kKnTp347rvv2LRpE++8845fh7XN1kcgTqpgtJMkJW6Qn59PbGysa9dHPXDuKGomxEkNzOakp8/Bgwfp27cvTz/9NMnJyezdu5cHHnhAt/rdxWx9BOKkCno6paWlERsbS35+vttlbjjRVYCQkBCWL1+ua52edJIqiJMamM1JDx/n6Mjvfvc72rVrx+bNm+nTp48O0XmH2foIxEkV9HRKTEwkMTHRo+0WZKTEIBo3bmx0CLojTmpgNqea+uTm5tKrVy9mzpzJjBkz2LNnj6EJCZivj0CcVMFopyBN0zRDI1CA2NhY3UdKysvLqVXLXDmhOKmB2Zy89bl27Rp/+tOfePnll7n77ruZN28ePXv29EGEnmO2PgJxUgVfOHlyDTXXu6kQCxYsMDoE3REnNTCbkzc+2dnZ9OzZkxdeeIEnnniC3bt3B0xCAubrIxAnVTDaSZISQRBuGq5evcqrr76K1Wrl4sWLZGZmMmvWLBo2bGh0aIIgIBNdDePee+81OgTdESc1MJuTuz779u1j0qRJ7Nu3j5SUFF588UUaNGjg4+i8w2x9BOKkCkY7yUiJQbRs2dLoEHRHnNTAbE438rly5Qq///3viYqK4urVq3z99de8/vrrAZuQgPn6CMRJFYx2kqTEILZu3Wp0CLojTmpgNqfqfHbv3k2PHj145ZVXeO6559i1axfdunXzY3TeYbY+AnFSBaOdJCkRBMF0XLlyhRdffJEePXoAsHPnTv7whz9Qv359gyMTBKE6ZE6JQURHRxsdgu6IkxqYzemHPrt27WLy5MkcOHCAF154geeee4569eoZFJ13mK2PQJxUwWgnGSkxiEOHDhkdgu6IkxqYzcnpU1paym9/+1vuv/9+6taty65du3jppZeUS0jAfH0E4qQKRjtJUmIQR48eNToE3REnNTCb09GjR9mxYwdWq5U333yTl19+me3bt9O5c2ejQ/Mas/URiJMqGO0kt28Mwoz3tsVJDczkdPnyZRYuXMjy5cvp2rUrdrudjh07Gh1WjTFTHzkRJzUw2kmWmXeDqKgoQkJCXJsLCYJgPNu2bWPKlCnk5eXx+9//nqeffpo6deTvLEEIFNLS0khLSyM/Px+73e5WGbl94wbOXYL1TEiMXsrXF4iTGqjudOnSJZ5++ml69+5N06ZNef3113n22WdNlZCo3keVIU5qoKdTYmIiy5cvJyQkxO0ykpQYRHl5udEh6I44qYHKTlu2bKFz587MmTOHWbNmsXXrVm6//Xajw9IdlfuoKsRJDYx2kqTEIMLCwowOQXfESQ1UdLpw4QJPPPEE/fr1o2XLluzZs4dnnnmGOnXqKOlzI8RJDcRJf8wz3qkYngxnqYI4qYFqTps3b2bKlCnk5+fz5ptv8utf/5ratWu7XlfNxx3ESQ3ESX9kpMQgNm/ebHQIuiNOaqCKU0lJCb/61a944IEHuO2229i7dy9PPfXU9xISUMfHE8RJDcRJf2SkRBCEgGPjxo088sgjnDx5krfeeosZM2ZQq5b8DSUIZkfOcoPo37+/0SHojjipQSA7nT9/nscee4yBAwfStm1b9u3bx+OPP15tQhLIPt4iTmogTvojSYlBGL1qni8QJzUIVKeMjAw6duzIRx99xJw5c9i4cSM/+clPblguUH1qgjipgTjpjyQlBpGXl2d0CLojTmoQaE7nzp0jKSmJmJgYQkND2b9/P7/85S/dvl0TaD56IE5qIE76I3NKDOKHk/XMgDipQSA5ffHFF0ybNo2ioiLeffddkpKSPJ47Ekg+eiFOaiBO+iPLzLtBbGwsy5cvNzoMQTANxcXF/OY3v+Ff//oX0dHRvP/++9x5551GhyUIgg/w5Boqt28MYtGiRUaHoDvipAZGO61atYrIyEhsNhvvvfcea9eurVFCYrSPLxAnNRAn/ZGkxCCuXLlidAi6I05qYJRTUVERkyZNYsSIEURGRpKdnc3UqVMJCgqqUb3SR2ogTmpgtJMkJW6Qn59PbGwsaWlputVpxqFqcVIDI5xWrFhBREQES5cu5YMPPmD16tW0a9dOl7qlj9RAnNRAT6e0tDRiY2PJz893u4wkJW7gi12Cjd5fwBeIkxr406mgoICHHnqI2NhYunbtSnZ2NlOmTKnx6EhFpI/UQJzUQE8n2SVYITZs2GB0CLojTmrgL6elS5cSERHB559/zocffsjKlSu54447dG9H+kgNxEkNjHaSpEQQBF05c+YMiYmJjB07lh49epCTk8PDDz+s6+iIIAjmRNYpMYg+ffoYHYLuiJMa+NIpPT2dxx57jLKyMj755BMefPBBnycj0kdqIE5qYLSTjJQYxOnTp40OQXfESQ184XT69GkmTJhAfHw8vXv3Jicnh5/97Gd+GR2RPlIDcVIDo50kKTGIQ4cOGR2C7oiTGujppGkaCxYsICIigg0bNpCWlsbixYtp06aNbm3cCOkjNRAnNTDaya2kJD09nZiYGGJiYoiKiiImJoaMjIwaN+5tvXrFk56eTlBQEFlZWd6ELwg3NadOnSIuLo6JEyfSv39/cnNzmThxoswdEQTBe7QbEBcXpwGa3W53/c5ms2mAlpKScqPiuterVzwOh0MDflRXZYwaNcrtegXB7JSXl2uffvqpFhwcrN16663awoULjQ5JEIQAxpNraLVJSVJSkgZoqampVb5ms9k8DtDbevWMx2q1GpqULFmyRPc6jUac1KAmTidOnNBiY2M1QJs4caJ2+vRp/QLzEukjNRAnNfCFkyfX0Cpv3+Tl5TF37lwAkpKSfvR6cnIyADNnzvRoZMbbevWMZ+bMmURHR2OxWDyKXU8uXbpkWNu+QpzUwBsnTdP4+OOPCQ8P5+uvv2bRokWkpaVx6623+iBCz5A+UgNxUgOjnapMSmbNmgVAdHR0pa9brVYsFgt5eXkezefwtl694snIyCAjI8NVn1H4YhEpoxEnNfDUKT8/n1GjRvHwww8zYsQIcnNzGTdunI+i8xzpIzUQJzUw2qnKpMR5YbdarVUWDg0NBSA1NdXtBr2tV494iouLSU5OxmazuR2vrwgPDzc6BN0RJzVw10nTNObNm0dERAR2u51ly5bxySef0KJFCx9H6Bk3cx+phDipgdFOlSYlxcXF5OXlAVT7BeRMAtx9esXbevWKJz4+nlmzZrmOM5K1a9caHYLuiJMauON07Ngxhg8fzpQpUxg9ejQ5OTnExsb6ITrPuVn7SDXESQ2Mdqp0RVdnAgBUO+8iODj4R8dXh7f16hHP3LlzsVgsxMXFuRVrRY4cOUJUVBQAt912G+Hh4XTt2pVWrVrRo0cPVq5cCVwfxSkvL2fPnj0AjB49mi1btlBQUEBwcDD9+vVj6dKlABw/fpxDhw5ht9sBGDFiBLt27eLUqVM0bdqUwYMHk56eDkBERARNmjRh+/btAAwZMoTs7Gzy8/Np3LgxI0eOZMGCBQDce++9tGzZkq1btwLXb3cdOnSIo0ePUr9+fcaNG8eCBQsoLy8nLCyMkJAQNm/eDED//v05evQoeXl51K5dmwkTJrBo0SKuXLnCnXfeSVhYmGtfhD59+nD69GnXM+2JiYns3LkTuD78Fx4e7vpw9+zZk7Nnz5KbmwtcTw7XrFnD+fPnadOmDVarlVWrVgHQvXt3Ll++zP79+wEYO3YsmzZtoqioiJYtW9KzZ09WrFgBQNeuXQHYvXs3AKNGjWLbtm2cOXOG5s2b079/f5YsWQJAx44dadCggSvG4cOHk5WVxcmTJ7nlllsYOnSoawQtPDycZs2asW3bNjIzMxk8eDC5ubkcP36chg0bMmbMGNeO0e3bt6dVq1Zs2bIFgIEDB+JwOPj222+pV68e48ePZ+HChZSVlREaGkq7du3YtGkTAP369SM/Px+Hw0GtWrVISEhg8eLFlJaW0q5dO9q3b+8aIezduzdnzpzh4MGDACQkJLBy5UouXLhASEgIkZGRfPHFFwDcd999lJSUkJOTA0BcXBxr167l3LlztG7dmosXL7rij4qK4urVq+zbt8/1mf3tb3/L3LlzadSoEYsWLaK0tJQvvviCLl26UKtWLVfSP3LkSHbs2MHp06dp1qwZgwYNYvHixQBERkbSqFEjduzYAcCwYcPYu3cvJ06coEmTJgwfPpyFCxcC0KFDB4KDg8nMzAQgJiaGAwcOcOzYMRo0aMDYsWOZP38+mqZxzz330KZNG7766isABgwYwOHDh0lLS6NOnTrEx8djs9m4du0ad999N3fddRcbN24EoG/fvpw8eZLDhw8TFBTExIkTWbJkCZcvX6Zt27Z06NCBdevWAdCrVy8KCws5cOAAABMmTGDVqlWUlJRw++2307lzZ1avXg1Ajx49uHjxItnZ2QCMGzeO9evXc/bsWa+/IzIzM+nUqRN169Y1zXcEXN8P6dKlS6b5jnB+Zs30HZGTk8O5c+f4/PPPgR9/R4wZM4bNmzdTWFhIixYt6NOnD8uWLQNwfUfMmTOHzMxMWrduTXFxMaWlpbhNZbNf161b53oypbqnWVJSUlzHFRUV3XBWrbf11jQeh8OhhYaG/uh4i8Vi2NM333zzje51Go04qUFVTt9++60WExOjAdqUKVPcOqcDgZupj1RGnNTAF066PH1jJmJiYgJiHklFzp49a3QIuiNOavBDJ03TSE1NJSIigv/85z+sXr2aDz74wNCn0zzhZugjMyBOamC0U6VJifM2CEBhYWGVhYuLi13/ducLzNt6axJPcnIyycnJ1U6QNQLnMKWZECc1qOj0zTffEB0dzfTp00lMTCQ7O5uhQ4caGJ3nmL2PzII4qYHRTpXOKak4EbTihf6HOBMEd/+i8rZeb8tlZGSwa9cuj54OEoSbgfLyct59911mzpxJixYtWLt2LTExMUaHJQjCTU6lIyUWi8WVCBQUFFRZ2JkgVLV2iF71elsuNTWVrKwsgoKCKv1xHh8VFeX6nb+Ij4/3W1v+QpzUwGq1MnDgQGbMmMHPf/5zsrOzlU5IzNhH4qQG4qQ/Vc4pcT6lUt3jvrt27QLw6AvN23q9KRcaGorVaq3yx0nF4/zFmjVr/NaWvxCnwKa8vJy3336bzp078+2337J+/XreffddbrnlFqNDqxFm6iMn4qQG4uQDqpoBW3HDuupet1gsHs3C9bZeX8Rj5NM3n332me51Go04BS6HDh3S+vbtqwFaTEyMdv78eaND0g2z9FFFxEkNxMk9dHn6JjQ01LXHjPNZ+Io4l2mvbLn2vLw8oqKiXPvR6FFvTeIJRNq0aWN0CLojToFHWVkZf/3rX+ncuTP5+fls3LiR5557jiZNmhgdmm6o3keVIU5qIE4+4EZZS1xcnGaxWDSHw+H6nXPdkJSUlErLVFwvpKpRCG/qrUm5yjBypKS4uFj3Oo1GnAKLAwcOaD179tSCgoK0X//611pJSYmmaWo7VYbZfDRNnFRBnNzDk2topU/fVMRmszF37lySk5OxWCyuyaF2u73KORgJCQmkp6e75mroVW9NygUaq1atIjEx0egwdEWcAoOysjL+8pe/8OKLL9K2bVs2b95Mnz59XK+r6FQdZvMBcVIFcdKfGyYlAElJSa5bJ+5gtVpxOBy611vTcj+kqKioxnUIQiDxn//8h8mTJ7Njxw6efPJJXnnlFRo1amR0WIIgCG5xU6zoGoh0797d6BB0R5yM49q1a/zxj3+ka9euFBcXs2XLFv785z9XmpCo4uQuZvMBcVIFcdIfSUoM4vLly0aHoDviZAzZ2dn07NmT559/nscff5zdu3fTq1evKo9XwckTzOYD4qQK4qQ/kpS4QX5+PrGxsa5dH/XAucOlmRAn/3L16lVee+01rFYrFy5cIDMzk9mzZ9OwYcNqywWykzeYzQfESRXEqXrS0tKIjY0lPz/f7TJuzSm52QkJCWH58uVGhyEILvbt28ekSZPYu3cvM2fO5MUXX6RBgwZGhyUIguAiMTGRxMREYmNj3S4TpGma5sOYTEFsbKzuScnly5dNdxERJ99z5coV3njjDV577TXuvfde5s2bR7du3TyqI9CcaorZfECcVEGc3MOTa6jcvjGITZs2GR2C7oiTb9mzZw89evTglVdeYebMmezatcvjhAQCy0kPzOYD4qQK4qQ/kpQYhBkfRxYn33DlyhVefPFFunfvjqZp7Nixg1deeYX69et7VV8gOOmJ2XxAnFRBnPRH5pQYRMuWLY0OQXfESX/sdjuTJ0/mP//5D88//zy//e1vqVevXo3qNNpJb8zmA+KkCuKkPzJSYhA9e/Y0OgTdESf9KC0t5fnnn+e+++6jdu3a7Ny5k5dffrnGCQmYr5/M5gPipAripD+SlBjEihUrjA5Bd8RJH3bu3InVauVPf/oTL7/8Mjt27KBLly661W+2fjKbD4iTKoiT/khSIggBwuXLl3n22We5//77adiwIXa7nRdeeIG6desaHZogCIJfkDklBtG1a1ejQ9AdcfKer7/+msmTJ5OXl8err77KM888Q506vjk9zdZPZvMBcVIFcdIfGSkRBAO5dOkSTz/9NL1796Zp06ZkZWXx3HPP+SwhEQRBCGQkKTGI3bt3Gx2C7oiTZ2zdupUuXbowZ84c3njjDbZu3UpERITP2nNitn4ymw+IkyqIk/5IUiIIfubixYs8+eST9O3bl+DgYPbs2UNKSoqMjgiCcNMjy8y7gS+WmS8pKaFJkya61mk04nRjNm/ezJQpU8jPz+e1117j17/+NbVr19atfncwWz+ZzQfESRXEyT1kmXmd8cUuwdu2bdOtrkBBnKrmwoULPP744zzwwAO0adOGvXv38tRTT/k9IQHz9ZPZfECcVEGcqkd2CfYRvtgl+MyZM7rWFwiIU+Vs3LiRRx55hJMnT/K3v/2NGTNmGJKMODFbP5nNB8RJFcSperzZJVhGSgyiefPmRoegO+L0fc6fP89jjz3GwIEDadu2Lfv27TPkds0PMVs/mc0HxEkVxEl/ZE6JG/hiTolsea0G3jqtX7+eRx55hP/973/MmjWLxx57jFq1AuNvALP1k9l8QJxUQZzcQ+aUKMCSJUuMDkF3xAnOnTtHcnIy0dHRhIaGsn//fmbMmBEwCQmYr5/M5gPipAripD8yp0QQdGLt2rVMnTqVoqIi3n33XZKSkgIqGREEQQh05BvTIDp27Gh0CLpzszqdPXuWqVOnMmTIEO69917279/P9OnTAzYhMVs/mc0HxEkVxEl/ZKTEIMx2HxJuTqdVq1aRlJTEuXPnmDt3LlOnTiUoKMhP0XmH2frJbD4gTqogTvoTmH/K3QTs3LnT6BB052ZyKioqYtKkSYwYMYLIyEiys7OZNm1awCckYL5+MpsPiJMqiJP+yEiJIHjIihUrSE5O5sKFC3zwwQdMnjxZiWREEAQh0JFHgt3AF48Enz17lmbNmulap9GY3amwsJBf//rXfPLJJwwfPpzU1FTuuOMOgyP0HLP1k9l8QJxUQZzcQx4JVoCsrCyjQ9AdMzstXbqU8PBwVq5cyYcffsjKlSuVTEjAfP1kNh8QJ1UQJ/2RpMQgTp48aXQIumNGp0OHDvHggw8yduxYevToQU5ODg8//LDSt2vM1k9m8wFxUgVx0h+ZU2IQt9xyi9Eh6I7ZnBYtWsSzzz5LUFAQn3zyCQ8++KDSyYgTs/WT2XxAnFRBnPRH5pS4QVRUFCEhIa7NhfTg2rVr1KljrpzQLE6nT59mxowZ2Gw2Ro8ezT//+U/atGljdFi6YZZ+cmI2HxAnVRCn6klLSyMtLY38/HzsdrtbZeT2jRs4dwnWKyEBsNlsutUVKKjupGkaCxcuJCIigg0bNpCWlsaECRNMlZCA+v30Q8zmA+KkCuJUPYmJiSxfvpyQkBC3y0hSIgjAqVOniIuLIyEhgf79+5OTk8PEiRNNcbtGEARBFcw17qQQ4eHhRoegOyo6aZrG/PnzmTFjBrVr12bhwoXEx8e7XlfR6UaYzclsPiBOqiBO+iMjJQZhtmfbQT2n7777jrFjx/Lggw8SExNDTk7O9xISUM/JHczmZDYfECdVECf9kaTEILZt22Z0CLqjipOmaXz88cdERESwbds2Fi1axPz587n11lt/dKwqTp5gNiez+YA4qYI46Y8kJcJNRX5+PrGxsTz88MMMGzaM3Nxcxo0bZ3RYgiAIAm4mJenp6cTExBATE0NUVBQxMTFkZGTUuHFv6/W23MyZM4mKiiIoKIiwsDCSk5MpLi6usYc3DB482JB2fUkgO2maxr///W8iIiLYtWsXS5cu5dNPP6VFixbVlgtkJ28xm5PZfECcVEGcfIB2A+Li4jRAs9vtrt/ZbDYN0FJSUm5UXPd6vSnncDi00NBQDaj0Z926ddXGOmrUKA/tbszmzZt1r9NoAtXp6NGj2rBhwzRAe/jhh7WCggK3ywaqU00wm5PZfDRNnFRBnNzDk2totSMlycnJpKenk5qaitVqdf0+Li6OpKQkZs+eTXp6useJkLf1elsuJiaG6OhoHA4HRUVF2O12kpKSXK/Hx8f7fcTk+PHjfm3PHwSak6ZpfPDBB0RGRrJ3717XvjXBwcFu1xFoTnpgNiez+YA4qYI46U+VSUleXh5z584F+N4F3ElycjJw/ZaIJ3hbr7flZs6cSVxcHKmpqYSGhmKxWLBaraSmpjJr1iwAiouLWbhwoUceNaVhw4Z+bc8fBJLT0aNHGTp0KFOnTmX8+PHk5OQwYsQIj+sJJCe9MJuT2XxAnFRBnHxAVUMoSUlJGqBFR0dXOcxisVjcuv2hR73elrNardXG4ywTFxdX5TG+uH0j+Iby8nLtn//8p9akSRPtjjvu0FavXm10SIIgCDc1uty+cU4crXib5IeEhoYCkJqa6nYS5G293pZ77733qo2nW7duAB4N6etBWlqaX9vzB0Y7/fe//yUmJobp06czceJEsrOzGTp0aI3qNNrJF5jNyWw+IE6qIE76U2lSUlxcTF5eHkC1Tyc4k4CsrCy3GvO23prEU10SA1BYWAhc33RPUJPy8nL+8Y9/EBkZyeHDh/niiy947733DF8ESBAEQfCMSpeZdyYAABaLpcrCztGFisdXh7f1+ioe+L8EprJ5Kk6OHDniSlpuu+02wsPD6dq1K61ataJHjx6sXLkSuJ4AlZeXs2fPHgBGjx7Nli1bKCgoIDg4mH79+rF06VIAateuzaFDh1w7J44YMYJdu3Zx6tQpmjZtyuDBg12TdiMiImjSpAnbt28HYMiQIWRnZ5Ofn0/jxo0ZOXIkCxYsAODee++lZcuWbN26FYDo6GgOHTrE0aNHqV+/PuPGjWPBggWUl5cTFhZGSEgImzdvBqB///4cPXqUvLw8ateuzYQJE1i0aBFXrlzhzjvvJCwsjA0bNgDQp08fTp8+zaFDh4DrGy+dOHGCtLQ07rjjDsLDw1m7di0APXv25OzZs+Tm5gLXJxavWbOG8+fP06ZNG6xWK6tWrQKge/fuXL58mf379wMwduxYNm3aRFFRES1btqRnz56sWLECgK5du3Ls2DGeeOIJcnNzeeSRRxg5ciQFBQWsWbOG/v37s2TJEgA6duxIgwYN2LlzJwDDhw8nKyuLkydPcssttzB06FDXRlTh4eE0a9aMbdu2kZeXR0FBAbm5uRw/fpyGDRsyZswY118T7du3p1WrVmzZsgWAgQMH4nA4+Pbbb6lXrx7jx49n4cKFlJWVERoaSrt27di0aRMA/fr1Iz8/H4fDQa1atUhISGDx4sWUlpbSrl072rdv7xoh7N27N2fOnOHgwYMAJCQksHLlSi5cuEBISAiRkZF88cUXANx3332UlJSQk5MDXJ8IvnbtWs6dO0fr1q25/fbbXfFHRUVx9epV9u3bB8CYMWPYvHkzhYWFtGjRgj59+rBs2TIAunTpQq1atVznzMiRI9mxYwenT5+mWbNmDBo0iMWLFwMQGRlJo0aN2LFjBwDDhg1j7969nDhxgiZNmjB8+HDXPK4OHToQHBxMZmYmcH1i+oEDBzh27BgNGjRg7NixzJ8/H03TuOeee2jTpg1fffUVAAMGDKC0tJS0tDTq1KlDfHw8NpuNa9eucffdd3PXXXexceNGAPr27cvJkyc5fPgwQUFBTJw4kSVLlnD58mXatm1Lhw4dWLduHQC9evWisLCQAwcOADBhwgRWrVpFSUkJt99+O507d2b16tUA9OjRg4sXL5KdnQ3AuHHjWL9+PWfPnvX6OyIvL4+cnBzq1q1rmu+I9u3bs3TpUi5duuTX7wiA3bt3AzBq1Ci2bdvGmTNnaN68eY2/I/Ly8khLS2Pw4MGm+Y44e/Ys586d4/PPP/f6O2LOnDlkZmbSunVriouLKS0txW0qu6ezbt061+OyNputyns/KSkpruOKiopueK/I23p9FY/zUeLU1NRqj/PFnJKjR4/qXqfR+NOprKxMe/vtt7VGjRppd911l7Z+/XqftCP9FPiYzUfTxEkVxMk9dHsk2OzMnDmT6OjoakdJfIUzazYT/nI6cuQI/fv35/HHH2fy5Mns37+fgQMH+qQt6afAx2w+IE6qIE76U2lSUnHSp3PORWVUXNujutsqNa3XF/HMnDkTi8XiGq4VAp+ysjL++te/0qlTJ/Lz89m4cSNz5syhSZMmRocmCIIg6EClc0qcE0aBahcVcyYI7iQkNalX73gyMjJIT0933as1Al/9ZW8kvnQ6ePAgU6ZMITMzk8cff5zXX3+dxo0b+6w9J9JPgY/ZfECcVEGc9KfSkRKLxeJKBAoKCqos7EwQoqOj3WrM23r1jCcrK4uZM2dit9vdTqZ8gcPhMKxtX+ELp7KyMt588026dOnC6dOn2bx5M2+99ZZfEhKQflIBs/mAOKmCOOlPlXNK4uLigOof9921axdwfba8u3hbrx7xZGVlMW3aNNavX29oQgLw7bffGtq+L9Db6T//+Q99+vQhJSWFRx99lL1799K3b19d27gR0k+Bj9l8QJxUQZz0p8qkxLlse1W77+bl5VFcXIzFYvFooqi39dY0nry8vBsmJM5l7P1BvXr1/NaWv9DL6dq1a8yaNYuuXbtSWFjIli1b+Mtf/kKjRo10qd8TpJ8CH7P5gDipgjj5gOoezXEu7V7ZY7jO1yp7nNbhcGhWq1VLSkrStd6axmO327WioqIf/TgcDm3WrFlVxivLzPuP7OxsrXv37lqtWrW0Z555Rrt48aLRIQmCIAg1wJNraLVJiaZpWlxcnGaxWDSHw+H6nXPdkJSUlErLVFwvxG6361avN+UcDodrf5sb/VQVqy+SkgULFuhep9HUxOnKlSvaq6++qtWrV0/76U9/qn399dc6RuY90k+Bj9l8NE2cVEGc3MOTa2ilT99UxGazMXfuXJKTk7FYLK7JpHa7vcol3BMSEkhPTyc0NLTKY7yp19NyxcXFREVFVfvEjhOr1XrDJen1pKyszG9t+Qtvnfbt28fkyZPZs2cPKSkpvPTSSzRo0EDn6LxD+inwMZsPiJMqiJP+3DApgetLsHsyb8Rqtbo1g9fTej0tZ7FYKCoq8rh+f1DxMWez4KnT1atXeeONN3j11Vdp3749X3/9Nd27d/dRdN4h/RT4mM0HxEkVxEl/3EpKBP1p166d0SHojidOe/bsca3G+uyzz/K73/2O+vXr+zA677jZ+0kFzOYD4qQK4qQ/N/Uy80bi3HDJTLjjdOXKFV566SW6d+9OWVkZ27dv59VXXw3IhARu3n5SCbP5gDipgjjpjyQlbpCfn09sbKxr10fBO7KysujWrRuvv/46zz//PLt27XLtviwIgiCYi7S0NGJjY8nPz3e7TJCmaZoPYzIFsbGxLF++XNc68/PzCQkJ0bVOo6nKqbS0lFdeeYU//vGPdOzYkXnz5tGlSxf/B+gFN1M/qYrZfECcVEGc3MOTa6iMlBiEJ5mjKlTmtHPnTqKiopg9ezYvvfQSO3bsUCYhgZunn1TGbD4gTqogTvojSYlBGL2/gC+o6HT58mWeffZZ7r//furXr4/dbud3v/sddevWNTBCzzF7P5kBs/mAOKmCOOmPPH1jELVqmS8fdDp9/fXXTJ48mby8PF555RWeeeYZ5ZIRJ2buJ7NgNh8QJ1UQJ/2ROSVu4Is5JWbk0qVLvPjii/zlL38hKiqKefPmERERYXRYgiAIgoHInBIFWLx4sdEh6EpmZiY/+clPeOedd3jjjTfIzMw0RUJitn4C8zmZzQfESRXESX8kKTGI0tJSo0PQhYsXL/LUU0/Rp08fGjVqxO7du0lJSaFOHXPcGTRLP1XEbE5m8wFxUgVx0h9zXDkUxOhV8/Tgq6++YsqUKRw/fpw//elPdO/enZ/+9KdGh6UrZuinH2I2J7P5gDipgjjpj4yUGET79u2NDsFrLly4wOOPP84DDzxA69at2bt3L7/5zW9Ml5CA2v1UFWZzMpsPiJMqiJP+SFJiEBkZGUaH4BWbNm2iU6dOvP/++/zlL3/hyy+/dH2IVXWqDnEKfMzmA+KkCuKkP5KUCG5RUlLCL3/5SwYMGEBISAj79u3jiSeeoHbt2kaHJgiCIJgEmVNiEL179zY6BLdZv349U6dO5fTp07zzzjs89thjlT7LrpKTu4hT4GM2HxAnVRAn/ZGREoM4c+aM0SHckHPnzjF9+nSio6O566672L9/PzNmzKhycR0VnDxFnAIfs/mAOKmCOOmPJCVu4Itdgg8ePKhbXb5g7dq1REZG8sknn/CPf/yD9evXExoaWm2ZQHfyBnEKfMzmA+KkCuJUPd7sEixJiRuEhISwfPlyEhMTjQ7F55w9e5apU6cyZMgQ2rdvT3Z2No8++qjhSw8LgiAIapGYmMjy5cs92nVYlpl3A18sM19eXh5wF/rVq1eTlJTE2bNnefPNN5k2bRpBQUFulw9Ep5oiToGP2XxAnFRBnNxDlplXgJUrVxodgouioiImT57M8OHDCQ8PJzs7m6SkJI8SEggsJ70Qp8DHbD4gTqogTvojT98YxIULF4wOAbj+AUxOTqakpIQPPviAyZMne5yMOAkUJz0Rp8DHbD4gTqogTvojIyUG4ck9Nl9QWFjIww8/zKhRo+jcuTM5OTlMmTLF64QEjHfyBeIU+JjNB8RJFcRJf2SkxCAiIyMNa3vZsmVMnz6dS5cu8e9//5uHH364RsmIEyOdfIU4BT5m8wFxUgVx0h8ZKTGIL774wu9tnjlzhgcffJAxY8bQrVs3cnNz+cUvfqFLQgLGOPkacQp8zOYD4qQK4qQ/MlJyk7B48WIeffRRrl69yscff8zPfvYz3ZIRQRAEQdADGSkxiPvuu88v7fzvf/8jISGB8ePH06tXL3JycnjooYd8kpD4y8mfiFPgYzYfECdVECf9kZESgygpKfF5GzabjcceewxN00hLSyMhIcGnoyP+cPI34hT4mM0HxEkVxEl/ZKTEIHJycnxW96lTp4iLi2PChAk88MAD5OTkMHHiRJ/frvGlk1GIU+BjNh8QJ1UQJ/2RkRIToWka8+fP51e/+hVBQUEsXLiQ+Ph4o8MSBEEQBLeQZebdwBfLzF+9epW6devqVt/Jkyd59NFHWbp0KRMmTGDOnDnceuututXvDno7BQLiFPiYzQfESRXEyT1kmXmd8cUuwWvXrtWlHk3T+OSTTwgPDyczM5P09HQWLFjg94QE9HMKJMQp8DGbD4iTKohT9cguwT7CF7sEnzt3rsZ1nDhxgtjYWH7+858zbNgwcnJyGD9+vA7ReYceToGGOAU+ZvMBcVIFcaoeb3YJljklBtG6dWuvy2qaxkcffcQTTzxBgwYNWLp0KaNHj9YxOu+oiVOgIk6Bj9l8QJxUQZz0R0ZKDKJbt25elTt+/DgjRoxg0qRJjBo1ipycnIBISMB7p0BGnAIfs/mAOKmCOOmPJCUG8fnnn3t0vKZpfPDBB0RERLB3715WrFjBRx99RHBwsI8i9BxPnVRAnAIfs/mAOKmCOOmPJCUKcPToUYYOHcrUqVMZN24c2dnZjBw50uiwBEEQBEFXJCkxiKioqBseo2kac+fOJTIykpycHFatWsW8efNo3ry5HyL0HHecVEOcAh+z+YA4qYI46Y9bSUl6ejoxMTHExMQQFRVFTEwMGRkZNW7c23r9Xc4XXL16tdrX//vf/xITE0NycjIJCQnk5OQwbNgwP0XnHTdyUhFxCnzM5gPipAri5AO0GxAXF6cBmt1ud/3OZrNpgJaSknKj4rrX6+9ymqZpo0aNckfJIz777LNKf19WVqb94x//0Bo3bqy1bdtW++KLL3Rv21dU5aQy4hT4mM1H08RJFcTJPTy5hlY7UpKcnEx6ejqpqalYrVbX7+Pi4khKSmL27Nmkp6d7nAh5W6+/y/mbvLw8Bg0axGOPPcZDDz1EdnY2gwcPNjosQRAEQfAPVWUrDodDA7SqDrHb7RqghYaGepQxeVuvv8tVxBcjJRcvXnT9u6ysTHv77be1Ro0aaXfeeaeWkZGhe3v+oKKTWRCnwMdsPpomTqogTu6hy0jJrFmzAIiOjq70davVisViIS8vz6N5Gd7W6+9yvmbz5s0AHDlyhAEDBvD4448zadIk9u/fz6BBg/wWh544ncyEOAU+ZvMBcVIFcdKfKpMS5wW64u2OHxIaGgpAamqq2w16W6+/y/maM2fO8Le//Y1OnTpx7NgxNm7cyN///nduueUWv8WgN4WFhUaHoDviFPiYzQfESRXESX8qTUqKi4vJy8sDoEWLFlUWdl7Ms7Ky3GrM23r9Xc7XHDx4kNdff50nn3ySadOmsX//fvr37++Xtn1Jde+xqohT4GM2HxAnVRAn/al07xvnhRzAYrFUWdi5mmjF46vD23r9Xe6HHDlyxPXs9m233UZ4eDhdu3alVatW9OjRg5UrVwLXR2PKy8vZs2cPAKNHj2bLli0UFBQQHBxMv379WLp0KbNmzeL8+fN8+umnBAUFsXz5ckaMGMGuXbs4deoUTZs2ZfDgwa7JtxERETRp0oTt27cDMGTIELKzs8nPz6dx48aMHDmSBQsWAHDvvffSsmVLtm7dCly/bXXo0CGOHj1K/fr1GTduHAsWLKC8vJywsDBCQkJcw3X9+/fn6NGj5OXlUbt2bSZMmMCiRYu4cuUKd955J2FhYWzYsAGAPn36cPr0aQ4dOgRc33ipqKiItLQ07rjjDsLDw127Tfbs2ZOzZ8+Sm5sLQHx8PGvWrOH8+fO0adMGq9XKqlWrAOjevTuXL19m//79AIwdO5ZNmzZRVFREy5Yt6dmzJytWrACga9euAOzevRuAUaNGsW3bNs6cOUPz5s3p378/S5YsAaBjx440aNCAnTt3AjB8+HCysrI4efIkt9xyC0OHDsVmswEQHh5Os2bN2LZtG6WlpRQUFJCbm8vx48dp2LAhY8aMce0Y3b59e1q1asWWLVsAGDhwIA6Hg2+//ZZ69eoxfvx4Fi5cSFlZGaGhobRr145NmzYB0K9fP/Lz83E4HNSqVYuEhAQWL15MaWkp7dq1o3379q6Rvt69e3PmzBkOHjwIQEJCAitXruTChQuEhIQQGRnJF198AcB9991HSUkJOTk5wPUJ3WvXruXcuXO0bt2aTp06ueKPiori6tWr7Nu3D4AxY8awefNmCgsLadGiBX369GHZsmUAdOnShVq1armS95EjR7Jjxw5Onz5Ns2bNGDRoEIsXLwYgMjKSRo0asWPHDgCGDRvG3r17OXHiBE2aNGH48OEsXLgQgA4dOhAcHExmZiYAMTExHDhwgGPHjtGgQQPGjh3L/Pnz0TSNe+65hzZt2vDVV18BMGDAAOrVq0daWhp16tQhPj4em83GtWvXuPvuu7nrrrvYuHEjAH379uXkyZMcPnyYoKAgJk6cyJIlS7h8+TJt27alQ4cOrFu3DoBevXpRWFjIgQMHAJgwYQKrVq2ipKSE22+/nc6dO7N69WoAevTowcWLF8nOzgZg3LhxrF+/nrNnz3r9HVFaWkpOTg5169bFbrcDKP8dMXr0aJYuXcqlS5dM8x1x4sQJ0tLSGDx4sGm+I5o1a8a5c+dcK7t68x0xZ84cMjMzad26NcXFxZSWluI2lU00WbdunWtyqM1mq3JCSkpKiuu4oqKiG05g8bZef5f7IXpPdP3uu++0efPm6VpnICCPx6mB2ZzM5qNp4qQK4uQeuj0SLPiGNm3aUL9+faPDEARBEISAotKkpOImb9VNeikuLnb9u7rbIzWt19/l/EGXLl380o4/ESc1MJuT2XxAnFRBnPSn0qTEOfETvn/B/iHOC727F3Jv6/V3OX9Qq5b5BqnESQ3M5mQ2HxAnVRAnH7Rf2S8tFovrgl5QUFBlYeeFvqo1QPSq19/l/IG/nvTxJ+KkBmZzMpsPiJMqiJP+VJkSxcXFAdUHuGvXLuD6bHl38bZef5cTBEEQBMG/BGmaplX2Ql5eHmFhYQBUdojzdYvFQlFRkdsNeluvv8tVJDY2luXLl7vt6A7nz59XeqG0yhAnNTCbk9l8QJxUQZzcw5NraJUjJaGhoSQlJQFUulmdc/l2538rkpeXR1RUFMnJybrV6+9yvsa5foOZECc1MJuT2XxAnFRBnHzAjZ4ZjouL0ywWi+ZwOFy/c67/kZKSUmmZiut+2O123eo1opym+WZDPnm+XQ3EKfAxm4+miZMqiJN7eHINrXRF14rYbDbmzp1LcnIyFovFNSnUbrdXuZ9MQkIC6enphIaGVnmMN/UaUc5X7N69m8TERL+360vESQ3M5mQ2HxAnVRAn/blhUgKQlJTkugXiDlarFYfDoXu9RpXzBc7llM2EOKmB2ZzM5gPipAripD/me8jaQJx7HbjDd999p3u9nrTvizrddfJFnJ4cq4qTr94nszn54lzy5FhVnIw+78TJXN/jntbrLpKU6IgvOsiTeo3+MOvdtq+OVcXJV++TL+oUJ/3b17tOo887TxAn4+r0BF/UW+UjwcL/ERER4XqsuDry8/MJCQlxq05fHHsz12l0+6rUaXT7qtRpdPuq1Gl0++KkxvvkcDhcOxLfCElKBEEQBEEICOT2jSAIgiAIAYEkJYIgCIIgBASSlAiCIAiCEBBIUiIIgiAIQkAgSYngMyrbayhQCOTY/I28F2oSyP0WyLH5E3kfPEeSEj+Snp5OTEwMMTExREVFERMTQ0ZGhtFhATBz5kyioqIICgoiLCyM5ORk11L81TF79myCgoIq/VmwYEGV5fzxXvg7Nl85FRcXV+nxw5+qvgSN7KesrCxiYmLIyspy63h/v//elPPUSYXzy1OnQD+/3PFR8dzy9rOkwnkF3HhDPkEf4uLifrRBoc1mc2tDQF/icDi00NBQ1waKP/xZt25dteUtFkuVZavbjNEf74U/Y/OlU2pqapUeP/wpKiqqtA4j+snhcLjqqK4dPdr0VzlPnVQ4v7zpJ3/F5k05T3xUOrdq8lkK9POqIpKU+IGkpCQN0FJTU6t8zWazGRCZpoWGhmpJSUmaw+HQioqKNLvd7ooJ0CwWS5UnY2pqqhYaGqo5HI5KfyrDX++FP2PztVNoaKiWkpJSZT8UFRVpFotFi46OrvR1I/opNTVVS0lJ8ehi5+/339Ny3jgF+vnljZO/YvOmnKc+Kp1b3n6WAv28+iGSlPgYh8Ph+tBUht1u1wAtNDTUz5FpWkpKSpVZ66xZs1xxV/bh0rTrJ4knF1t/vhf+is3XTna7/YZ/Waxbty5g+6liXdVdHPz9/tfE0V0nlc4vd538HZse5aryUenc8vazpNJ55USSEh/jzAyryrQ17f+G/240lKs3Vqu12tedccXFxf3oNedQXGpqqtvDvf56L/wZWyD0rzOGyv5KMrqfioqK3LrY+fv9r4mju04qnV/uOvk7Nm/LeeLjTtxGn1vefpZUOq+cSFLiY5z3AKvLyK1Wa5VfTr7kRidSdHS0BmhJSUk/es0Zc8WfuLi4Gw6V+uO98GdsgdC/1X0JGN1P7l4c/P3+18TRXSeVzi9PLuIqnF96JSWBcm55+1lS6bxyIk/f+JDi4mLy8vIAaNGiRZXHhYaGArg9610vrFZrta8XFhYCEBUV9b3fFxcXExwc7IrbSXp6OlFRUcycOfNHdfnrvfBnbIHQv87Z7PHx8T96LZD7SY82A73f5PyqWWxGn1+BdG5581lS9bySpMSHODsIwGKxVHlccHDwj44PBJwfmqSkpO/93mKxsG7dOhwOB0VFRdhstu+dNLNnz/7RSemv98KfsQVC/6ampgIwYcKEH70WyP2kR5sq9xvI+XWj2IzuJ5XOrco+S6qeV5KU+BBn9gr/1xGVUbED3Xne3B84n8l3nphVYbFYiIuLw263s27dOpfL7Nmzv5cJG/Fe+Dq2QOjf9PR0oqOjq/0ScMYQqP3k7/c/UPoN5PyqLjaj+0mVc6uqz5Kq55UkJUKlzJw5k+jo6B/9FVcd0dHR2O121/8HysJwENixeYvzy6iy4eXqMON7oRpyfgU2Kp1b3nyWAhlJSnxIxWyxYhb5QypmizfKyv3BzJkzXcOTnhIaGkpKSgoAO3fudP0+EN4LvWMz2sm5WmRlw8s3IpD6yd/vv9H9JueXe7EZ6aTKuVXdZ0nV80qSEh9ScRJUdcNwzg4MhIQkIyOD9PR01q9f73UdMTExwPedA+W90DM2o53cHV6uikDpJ3+//0b2m5xfxVRFIPWTCufWjT5Lqp5XkpT4EIvF4uqogoKCKo9zdmB0dLQ/wqqSrKwsZs6cid1ur9EJ7nSu+CENlPdCz9iMdPJ2eLkigdJP/n7/jeo3Ob/U6CcVzi13PkuqnleSlPiYuLg4oPrH1Xbt2gX8X3ZtBFlZWUybNo3169fX+C8Op+sPH3UMhPdC79iMcqruyQB3CaR+8vf7729HOb/U6CcI/HPLk8+SkudVlSucCLpwo2V3na9bLBY/R/b9GKxWa5X7P2iaVuUyy5WRlJRU6T4MgfBe6B2bEU7OhaGqWzXRHfzRT+4uYuXv978mjp4uzKXC+aXnCqiBcH556xPo55annyWVzisnkpT4AW83L/IHzg+53W7XioqKfvTjcDi0WbNmfW+lwFmzZmkpKSmVbjjl3NugqiWEff1eGBGbv/vXubPpjeoMhH7yZE+VQNocrLpynjipcn6566TK+eXpXj5OAvnc8uazVJP2jPo+lKTET8TFxWkWi+V7H2LnZk813dreWxwOR7Xbblf8qXhiV/y9c9dKh8Ohpaamuk6a6vDle2FUbP7sX+eS0tX9taRpgdFPzv1BqvqS0qtNf5Zz10ml88tdJ1XOL08/d04C9dzy9rPkbXtGldM0SUr8SmpqqhYdHa3FxcVp0dHRWnR0dI2GSmuCc0tudz7kP9wMymazfW/fB8v/39rbk9EAX70XRsbmj/71ZHjZyPfCZrNpKSkp3/uMWSwWLSUl5Ybt+/v9d7ecJ06qnF+e9lOgn181+dwF6rlVk8+SN+0ZXS5I0zQNQRAEQRAEg5GnbwRBEARBCAgkKREEQRAEISCQpEQQBEEQhIBAkhJBEARBEAICSUoEQRAEQQgIJCkRBEEQBCEgkKREEARBEISAQJISQRAEQRACAklKBEEQBEEICP4fmyV+93An7eUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use([\"science\", \"ieee\", \"grid\"])\n",
    "plt.subplots(figsize=(6, 4), dpi=100)\n",
    "plt.subplot(1, 1, 1)\n",
    "plt.plot(list(range(len(betas))), betas)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate all constants\n",
    "\n",
    "$\\gamma = \\prod_0^t \\alpha_i$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3441484/3436455520.py:5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  gammas = torch.tensor(gammas, dtype=torch.float32)\n"
     ]
    }
   ],
   "source": [
    "betas = torch.tensor(betas, dtype=torch.float32)\n",
    "alphas = 1.0 - betas\n",
    "(timesteps, ) = betas.shape\n",
    "gammas = np.cumprod(alphas, axis=0)\n",
    "gammas = torch.tensor(gammas, dtype=torch.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbcb821f070>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use([\"science\", \"ieee\", \"grid\"])\n",
    "plt.subplots(figsize=(6, 4), dpi=100)\n",
    "plt.subplot(1, 1, 1)\n",
    "plt.plot(list(range(len(gammas))), gammas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "gammas_prev = np.append(1.0, gammas[:-1])\n",
    "gammas_prev = torch.tensor(gammas_prev, dtype=torch.float32)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "calculations for diffusion $q(x_t | x_{t-1})$ and others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3441484/2244138204.py:3: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  sqrt_recip_gammas = torch.tensor(sqrt_recip_gammas, dtype=torch.float32)\n",
      "/tmp/ipykernel_3441484/2244138204.py:4: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  sqrt_recipm1_gammas = torch.tensor(sqrt_recipm1_gammas, dtype=torch.float32)\n"
     ]
    }
   ],
   "source": [
    "sqrt_recip_gammas = np.sqrt(1.0 / gammas)\n",
    "sqrt_recipm1_gammas = np.sqrt(1.0 / gammas - 1)\n",
    "sqrt_recip_gammas = torch.tensor(sqrt_recip_gammas, dtype=torch.float32)\n",
    "sqrt_recipm1_gammas = torch.tensor(sqrt_recipm1_gammas, dtype=torch.float32)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "calculations for posterior $q(x_{t-1} | x_t, x_0)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3441484/2843208340.py:2: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  posterior_variance = torch.tensor(posterior_variance, dtype=torch.float32)\n"
     ]
    }
   ],
   "source": [
    "posterior_variance = betas * (1.0 - gammas_prev) / (1.0 - gammas)\n",
    "posterior_variance = torch.tensor(posterior_variance, dtype=torch.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbcabb33f10>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use([\"science\", \"ieee\", \"grid\"])\n",
    "plt.subplots(figsize=(6, 4), dpi=100)\n",
    "plt.subplot(1, 1, 1)\n",
    "plt.plot(list(range(len(posterior_variance))), posterior_variance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3441484/148861373.py:2: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  posterior_log_variance_clipped = torch.tensor(posterior_log_variance_clipped, dtype=torch.float32)\n"
     ]
    }
   ],
   "source": [
    "posterior_log_variance_clipped = np.log(np.maximum(posterior_variance, 1e-20))\n",
    "posterior_log_variance_clipped = torch.tensor(posterior_log_variance_clipped, dtype=torch.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbcab811c90>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use([\"science\", \"ieee\", \"grid\"])\n",
    "plt.subplots(figsize=(6, 4), dpi=100)\n",
    "plt.subplot(1, 1, 1)\n",
    "plt.plot(list(range(len(posterior_log_variance_clipped))), posterior_log_variance_clipped)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3441484/3021464480.py:2: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  posterior_mean_coef1 = torch.tensor(posterior_mean_coef1, dtype=torch.float32)\n"
     ]
    }
   ],
   "source": [
    "posterior_mean_coef1 = betas * np.sqrt(gammas_prev) / (1.0 - gammas)\n",
    "posterior_mean_coef1 = torch.tensor(posterior_mean_coef1, dtype=torch.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbcab7abc70>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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a2kpcXByLFy9m//79AMyfPx+AL774AoC1a9dSUVHBzZs3GTduHJmZmezZsweAl19+mfDwcCorKwFYtWoVNTU1NDU1ERUVxRtvvEFJSQkAqampREdHU1FRwenTp/nNb35DXV0d165dIyIignXr1vk+8W3GjBnEx8dTXl4OwGuvvYbb7ebKlSuEhoby9ttvU1xcTF9fH8nJySQlJXH8+HEAli1bRmNjI263G5fLRX5+Prt376arq4ukpCRmzJjBkSNHAHj11Ve5efMmX3/9NQD5+fkcOHCAu3fvkpiYyJw5czh06BAAr7zyCh0dHZw/fx6A3NxcDh8+zO3bt0lISOCbb75h9OjRAKSnp9PT08NXX30FwLp16zhx4gQtLS2MHz+ejIwMPvroIwDmzZuHy+Xy/fyuWbOGM2fO0NzcTHR0NK+//jq7d+8GYM6cOYwePZozZ84A8Oabb/Lll19y/fp1IiMjWbVqle8jAGfNmkVsbKzv8IkrVqzgwoULXL16lfDwcNavX8+HH36I1+tl+vTpTJgwgZMnTwKwfPlyLl++zJ/+9CeWLVtGXl4eJSUl9Pb2Mm3aNKZOneq7jS1dupSmpiYuXrxIUFAQ77zzDnv27KGzs5PJkycza9YsysrKAFiyZAktLS2+96xu3LiRgwcP0tHRwcSJE5k7dy6ffPIJAIsWLeLevXvU1tYCsGHDBj777DPa29uf+T7i9OnT/PznP2fUqFFUV1cbcR8BEBERwf379425j3jvvfdYsmQJ2dnZxtxHnD9/ns2bN/Pxxx8Dz3Yf8f7773P69GkSEhJoa2ujq6uLQbxDyM3N9ZaUlPi+Tk5O9lZXVz92vm3btnm3bdvmjYmJ8ZaVlQ21Ke/atWuHPP1ZHThwwAt4GxsbLd2u0z744AOnl2A5bZLBtCbTerxebZLCH02PztAhHx5PTk6mpaXF93VLS8tjLzKrqanh1q1bbN68mZKSErZt2zbUpixn6sPjy5cvd3oJltMmGUxrMq0HtEkKO5qGHNr5+fm+h5Xa2tpYsGCB73sDz223tLT4/p6VlTVoyPuTqUP78uXLTi/Bctokg2lNpvWANklhR9OQQzstLY2UlBRKS0v5j//4j0GvDB94fjorKwuAnTt3sn37dt3Tfk7ffPON00uwnDbJYFqTaT2gTVLY0TTs+7Q3b94MPHiS/WFut9v3d7ve5vUwU4d2SMiwV4VY2iSDaU2m9YA2SWFHk7jDmB4+fJiVK1dy+fJlpkyZYtl2lVJKqUCjhzENUANvhzCJNslgWpNpPaBNUtjRpEM7QPT29jq9BMtpkwymNZnWA9okhR1NOrQDxLRp05xeguW0SQbTmkzrAW2Swo4mHdoBYurUqU4vwXLaJINpTab1gDZJYUeTDu0AMXC4SJNokwymNZnWA9okhR1NOrSVUkopIXRoB4ilS5c6vQTLaZMMpjWZ1gPaJIUdTX4f2o2NjeTk5Pg+teV5mTq0m5qanF6C5bRJBtOaTOsBbZLCyqbCwkJycnJobGwcdLrfh3ZiYiL79u2joKDAku2ZOrQvXrzo9BIsp00ymNZkWg9okxRWNhUUFLBv3z4SExMHna4PjweIgS6TaJMMpjWZ1gPaJIUdTeIOY3rq1CkyMjI4f/48qamplm1XKaWUCjR6GNMAtWfPHqeXYDltksG0JtN6QJuksKNJh3aA6OzsdHoJltMmGUxrMq0HtEkKO5p0aAeIyZMnO70Ey2mTDKY1mdYD2iSFHU06tAPErFmznF6C5bRJBtOaTOsBbZLCjiYd2gGirKzM6SVYTptkMK3JtB7QJinsaNKhrZRSSgmhQztALFmyxOklWE6bZDCtybQe0CYp7GjSoR0gWlpanF6C5bRJBtOaTOsBbZLCjiYd2gHiwoULTi/Bctokg2lNpvWANklhR5MObaWUUkoIcYcxPXv2LPPnz+fMmTMsXLjQsu06ra+vj+DgYKeXYSltksG0JtN6QJuk8EeT7Ycx1Y/mfDoHDx50egmW0yYZTGsyrQe0SQorm4b7aM4Qyy5hGAMfzWkVU4d2R0eH00uwnDbJYFqTaT2gTVJY2VRQUEBBQQE5OTmDTtfntAPExIkTnV6C5bRJBtOaTOsBbZLCjiYd2gFi7ty5Ti/Bctokg2lNpvWANklhR5MO7QDxySefOL0Ey2mTDKY1mdYD2iSFHU06tJVSSikhdGgHiEWLFjm9BMtpkwymNZnWA9okhR1NOrQDxL1795xeguW0SQbTmkzrAW2Swo4mHdoBora21uklWE6bZDCtybQe0CYp7GjSoa2UUkoJIe4wphcvXmTGjBkcO3aMzMxMy7brtK6uLsLCwpxehqW0SQbTmkzrAW2Swh9Nth/G1Gqm7ml/9tlnTi/Bctokg2lNpvWANklhR5MO7QDR3t7u9BIsp00ymNZkWg9okxR2NOnQDhDx8fFOL8Fy2iSDaU2m9YA2SWFHk37KV4DQ9yzKoE2Bz7Qe0CYprGwa7lO+/D60Bz7lq6CgwJLtmTq0Dxw44PQSLKdNMpjWZFoPaJMUVjYVFBSwb98+EhMTB52uD48rpZRSQujQDhBpaWlOL8Fy2iSDaU2m9YA2SWFHkw7tANHf3+/0EiynTTKY1mRaD2iTFHY06dAOEGfPnnV6CZbTJhlMazKtB7RJCjuadGgrpZRSQujQDhBvvfWW00uwnDbJYFqTaT2gTVLY0aRDO0CUl5c7vQTLaZMMpjWZ1gPaJIUdTTq0A8StW7ecXoLltEkG05pM6wFtksKOJh3aASI2NtbpJVhOm2Qwrcm0HtAmKexoGnZob9++ndLSUrZs2UJbW9uwG2hra2Pnzp3U1NT4Y32PMXVoL1u2zOklWE6bZDCtybQe0CYp7GgacmjX1NTgdrvJzc1l69at5OXlDfmPa2pqePfdd9m0aZNtb5Q3dWjv3bvX6SVYTptkMK3JtB7QJinsaAoZ6sSioiLS09MBiImJoaqqira2NmJiYnznaWtrIy8vj+rqar8v8mGmDm2llFLqSYYc2h6Ph4ULF/q+jo2NxePxDNqbPnLkCAA7d+6krKyMvLw8Nm3a9Ni2Ll265PsF4MUXXyQ1NZX58+cTHx/PokWLfAdYT0tLo7+/3/fm9Lfeeovy8nJu3bpFbGwsy5YtY+/evdy5cwd48OlhA58ctnr1aqqqqrhx4wZjx44lOzub0tJSAGbPnk1kZCSff/45ACtXrqS2tpbGxkbGjBnDmjVrKCoqAmDmzJnExcVx6tQpALKysqivr6ehoYGwsDA2bNhAUVER/f39pKSkkJiYyIkTJwDIzMykoaEBj8dDcHAwGzduZNeuXXR3dzNlyhRSUlI4evQoABkZGTQ3N1NfXw88ODD8d999R2FhIZMmTSI1NZXDhw8DsHjxYtrb26mrqwMgLy+PTz/9lDt37jBhwgTS0tI4ePAgAAsXLqSzs5Nz584BsH79eo4fP05raytxcXEsXryY/fv3AzB//nwAvvjiCwDWrl1LRUUFN2/eZNy4cWRmZrJnzx4AXn75ZcLDw6msrARg1apV1NTU0NTURFRUFG+88QYlJSUApKamEh0dTUVFBdeuXePWrVvU1dVx7do1IiIiWLdune96mzFjBvHx8b5XXL722mu43W6uXLlCaGgob7/9NsXFxfT19ZGcnExSUhLHjx8HHjwM1djYiNvtxuVykZ+fz+7du+nq6iIpKYkZM2b4fkZfffVVbt68yddffw1Afn4+Bw4c4O7duyQmJjJnzhwOHToEwCuvvEJHRwfnz58HIDc3l8OHD3P79m0SEhJITk72rT89PZ2enh6++uorANatW8eJEydoaWlh/PjxZGRk8NFHHwEwb948XC6X72mkNWvWcObMGZqbm4mOjub1119n9+7dAMyZM4fRo0dz5swZAN58802+/PJLrl+/TmRkJKtWraK4uBiAWbNmERsby+nTpwFYsWIFFy5c4OrVq4SHh7N+/Xo+/PBDvF4v06dPZ8KECZw8eRKA5cuXc/nyZa5du0ZJSQl5eXmUlJTQ29vLtGnTmDp1KseOHQNg6dKlNDU1cfHiRYKCgnjnnXfYs2cPnZ2dTJ48mVmzZlFWVgbAkiVLaGlp4cKFCwBs3LiRgwcP0tHRwcSJE5k7dy6ffPIJ8OBTke7du0dtbS0AGzZs4LPPPqO9vf2Z7yOuXbvG+fPnGTVqlG+nQvp9xI9//GP27t3L/fv3jbmPuHbtGoWFhWRnZxtzH3H//n1u377Nxx9//Mz3Ee+//z6nT58mISGBtrY2urq6GMQ7hNzcXG9JSYnv6+TkZG91dfWg82zatMm7bds2r9fr9ba2tnqH2ZR37dq1Q57+rG7duuUFvKWlpZZu12lff/2100uwnDbJYFqTaT1erzZJ4Y+mR2fokM9pJycn09LS4vu6paXlseesH36oPCYmhuTkZDwez1Cbs5SpD4/b/TSDHbRJBtOaTOsBbZLCjqYhh3Z+fr7vwtva2liwYIHvewOvJM/Pz/c9FAIPBntycrIfl/qAqUNbKaWUepIhn9NOS0sjJSWF0tJSKisr2bFjh+976enpuN1u0tLSWLhwITt37gTwPV/hb6YO7dWrVzu9BMtpkwymNZnWA9okhR1Nw75Pe/PmzeTm5rJt27ZBe9But3vQeTZt2sSmTZvIysry70r/f6YO7aqqKqeXYDltksG0JtN6QJuksKNJj4gWIG7cuOH0EiynTTKY1mRaD2iTFHY06dAOEGPHjnV6CZbTJhlMazKtB7RJCjuadGgHiOzsbKeXYDltksG0JtN6QJuksKNJh3aAGDjQg0m0SQbTmkzrAW2Swo4mHdpKKaWUEDq0A8Ts2bOdXoLltEkG05pM6wFtksKOJh3aASIyMtLpJVhOm2Qwrcm0HtAmKexo0qEdIAY+rMAk2iSDaU2m9YA2SWFHk9+HdmNjIzk5Ob5PbXlepg5tpZRSakBhYSE5OTk0NjYOOj3I6+fpl5OTw759+yzbXm9vL6NGjeIPf/gDP/nJTyzbrtNaWlqIjY11ehmW0iYZTGsyrQe0SQp/ND06Q/Xh8QAx8HnCJtEmGUxrMq0HtEkKO5p0aAeIRx8CMYE2yWBak2k9oE1S2NGkQztAjBkzxuklWE6bZDCtybQe0CYp7GgS95w2PBjcO3fu5N1337V0u07q7+/H5RL3O9T30iYZTGsyrQe0SQp/NIl/TnuAaXvaRUVFTi/Bctokg2lNpvWANklhR5PIoR0UFGTc0FZKKaWeRId2gJg5c6bTS7CcNslgWpNpPaBNUtjRpEM7QMTFxTm9BMtpkwymNZnWA9okhR1NIoc2mPec9qlTp5xeguW0SQbTmkzrAW2Swo4mHdpKKaWUECKHdnBwsHFDOysry+klWE6bZDCtybQe0CYp7GgSObTBvD3t+vp6p5dgOW2SwbQm03pAm6Swo0ncp3zBg4Ft2tBuaGhwegmW0yYZTGsyrQe0SQorm4b7lK8Qyy5hGImJiX45IpppQzssLMzpJVhOm2Qwrcm0HtAmKaxsKigooKCggJycnEGnizyM6ZgxY/jtb3/Lr371K0u3q5RSSgUSIw5j2tfX5/QSLKeH9JNBmwKfaT2gTVLoYUyHYeLD4/39/U4vwXLaJINpTab1gDZJYUeTyKHtcrmMG9opKSlOL8Fy2iSDaU2m9YA2SWFHk8ihbeL7tBMTE51eguW0SQbTmkzrAW2Swo4mkUO7r6/PuKF94sQJp5dgOW2SwbQm03pAm6Swo0nk0AbzDq6ilFJKPYnIoR0WFmbcixgyMzOdXoLltEkG05pM6wFtksKOJpFDG8x725ceHUgGbQp8pvWANklhR5PIod3f32/cnrbH43F6CZbTJhlMazKtB7RJCjuaRA5tl8tl3J52cHCw00uwnDbJYFqTaT2gTVLY0STyMKZJSUn85Cc/4d/+7d8s3a5SSikVSIw4jGlnZ6dxe9q7du1yegmW0yYZTGsyrQe0SQo7mkR+NGdQUJBxz2l3d3c7vQTLaZMMpjWZ1gPaJIWVTUZ9NGdoaKhxe9pTpkxxegmW0yYZTGsyrQe0SQorm4b7aE6RD4+Hh4cbN7T1OLwyaFPgM60HtEkKPfb4MO7fv2/cw+NHjx51egmW0yYZTGsyrQe0SQo7mkQObRPf8qWUUko9icihHRUVZdyedkZGhtNLsJw2yWBak2k9oE1S2NEkcmj39fUZt6fd3Nzs9BIsp00ymNZkWg9okxR2NIkc2j09PcYN7fr6eqeXYDltksG0JtN6QJuksKNJ5NA28X3aSiml1JOIPIzpkiVLmDVrFn/4wx8s3a5SSikVSJ76MKbbt2+ntLSULVu20NbW9r0bXbFixRPPY6XW1lbj9rT37t3r9BIsp00ymNZkWg9okxR2NA05tGtqanC73eTm5rJ161by8vKG3UBpaakjH7Fm2nPa9+/fd3oJltMmGUxrMq0HtEkKO5qGHNpFRUWkp6cDEBMTQ1VV1ZB70gOnxcTE+Gt9Q4qIiDBuaE+aNMnpJVhOm2Qwrcm0HtAmKexoGnJoezweYmNjfV/HxsYOuTddXFxMbm6u/1Y3DBPfp52amur0EiynTTKY1mRaD2iTFHY0PfMHhhw5coSsrKwnnu/SpUu+vfYXX3yR1NRU5s+fT3x8PIsWLeLAgQMApKWl0d/fz9mzZwF46623KC8v59atW8TGxrJs2TLf8wVNTU1ERET4Pjls9erVVFVVcePGDcaOHUt2djalpaUAzJ49m8jISD7//HMAVq5cSW1tLY2NjYwZM4Y1a9ZQVFQEwMyZM4mLi+PUqVMAZGVlUV9fT0NDA2FhYWzYsIGioiL6+/tJSUkhMTGREydOAJCZmUlDQwMej4fg4GA2btzIrl276O7uZsqUKaSkpPgOcZeRkUFzc7Pv7QEFBQX89re/ZeHChUyaNInU1FQOHz4MwOLFi2lvb6eurg6AvLw8Pv30U+7cucOECRNIS0vj4MGDACxcuJDOzk7OnTsHwPr16zl+/Ditra3ExcWxePFi9u/fD8D8+fMB+OKLLwBYu3YtFRUV3Lx5k3HjxpGZmcmePXsAePnllwkPD6eyshKAVatWUVNTQ1NTE1FRUbzxxhuUlJQAD35oo6Ojqaio4PTp0/zmN7+hrq6Oa9euERERwbp163zX24wZM4iPj6e8vByA1157DbfbzZUrVwgNDeXtt9+muLiYvr4+kpOTSUpK4vjx4wAsW7aMxsZG3G43LpeL/Px8du/eTVdXF0lJScyYMYMjR44A8Oqrr3Lz5k2+/vprAPLz8zlw4AB3794lMTGROXPmcOjQIQBeeeUVOjo6OH/+PAC5ubkcPnyY27dvk5CQwDfffMPo0aMBSE9Pp6enh6+++gqAdevWceLECVpaWhg/fjwZGRl89NFHAMybNw+Xy0VNTQ0Aa9as4cyZMzQ3NxMdHc3rr7/O7t27AZgzZw6jR4/mzJkzALz55pt8+eWXXL9+ncjISFatWkVxcTEAs2bNIjY2ltOnTwMPXl9y4cIFrl69Snh4OOvXr+fDDz/E6/Uyffp0JkyYwMmTJwFYvnw5ly9f5k9/+hPLli0jLy+PkpISent7mTZtGlOnTuXYsWMALF26lKamJi5evEhQUBDvvPMOe/bsobOzk8mTJzNr1izKysqABy8UbWlp4cKFCwBs3LiRgwcP0tHRwcSJE5k7dy6ffPIJAIsWLeLevXvU1tYCsGHDBj777DPa29uf+T7i9OnT/PznP2fUqFFUV1cbcR8BDx5hvH//vjH3Ee+99x5LliwhOzvbmPuI8+fPs3nzZj7++GPg2e4j3n//fU6fPk1CQgJtbW10dXUxiHcImzdv9u7YscP3dUxMzGPnycrK8v2JiYnxZmVled1u92PnW7t27VAX8Vzmzp3r3bBhg+XbddIHH3zg9BIsp00ymNZkWo/Xq01S+KPp0Rk65J52fn4+O3bsAB48b71gwQLf99ra2oiJifH9Vj3w20RJSYltz22PHz/euOe0Fy9e7PQSLKdNMpjWZFoPaJMUdjQNObTT0tJISUmhtLSUyspK3wCHBwPa7Xb7fWHfp6+vz7jntNvb251eguW0SQbTmkzrAW2Swo6mYd+nvXnzZnJzc9m2bRvJycm+04ca2NXV1ba+gvzu3bvG7WkPPB9lEm2SwbQm03pAm6Swo0nsYUxNG9pKKaXUk4g8jGleXh7t7e2+V0+aoLe3l5CQZ34xf0DSJhlMazKtB7RJCn80PfVhTANZc3OzcXvan376qdNLsJw2yWBak2k9oE1S2NEkcmj39vYa90K0O3fuOL0Ey2mTDKY1mdYD2iSFHU0ih/aYMWOM29OeMGGC00uwnDbJYFqTaT2gTVLY0SRyaMfFxRk3tNPS0pxeguW0SQbTmkzrAW2Swo4mkUP722+/Ne7h8YHDDJpEm2Qwrcm0HtAmKexoEjm09S1fSimlfohEDu2EhATj9rQXLlzo9BIsp00ymNZkWg9okxR2NPl9aDc2NpKTk+P71BYreL1e4/a0Ozs7nV6C5bRJBtOaTOsBbZLCyqbCwkJycnJobGwcdLrfh3ZiYiL79u2joKDAsm22trYat6c98FF5JtEmGUxrMq0HtEkKK5sKCgrYt28fiYmJg04X+fC4y+Uybk9bKaWUehKRhzH95S9/SVlZGX/9618t3a6TOjs7CQ8Pd3oZltImGUxrMq0HtEkKfzQZcRjT69evG/fw+PHjx51eguW0SQbTmkzrAW2Swo4mkUO7u7vbuIfHW1tbnV6C5bRJBtOaTOsBbZLCjiaRQ3vMmDHG7WnHxcU5vQTLaZMMpjWZ1gPaJIUdTSKH9uTJk43b0168eLHTS7CcNslgWpNpPaBNUtjRJHJou91uent7nV6Gpfbv3+/0EiynTTKY1mRaD2iTFHY0iRzawcHBxg1tpZRS6klEDu3JkyfT09Pj9DIsNX/+fKeXYDltksG0JtN6QJuksKNJ5NAeNWqUcUNbKaWUehKRQ7upqcm4of3FF184vQTLaZMMpjWZ1gPaJIUdTSKHdnBwsHFDWymllHoSvx/GND09ncTERAoKCiz70JD/+Z//4Re/+AV9fX24XCJ/73hMR0cHkZGRTi/DUtokg2lNpvWANklhZVNhYSGFhYU0NjZSXV3tO13kp3xduXIFwKi97YqKCqeXYDltksG0JtN6QJuksLLJqE/5un//PmDW0L5586bTS7CcNslgWpNpPaBNUtjRJHJojx07FjBraI8bN87pJVhOm2Qwrcm0HtAmKexoEjm0586dC2DUAVYyMzOdXoLltEkG05pM6wFtksKOJpFDu6qqCjBrT3vPnj1OL8Fy2iSDaU2m9YA2SWFHk8ihHRwcDJg1tJVSSqknETm0p0+fDpg1tF9++WWnl2A5bZLBtCbTekCbpLCjSeTQHj16NGDW0A4PD3d6CZbTJhlMazKtB7RJCjuaRA7tixcvAmYN7crKSqeXYDltksG0JtN6QJuksKNJ5NDW57SVUkr9EIkc2gMvqzdpaK9atcrpJVhOm2Qwrcm0HtAmKexoEjm0TXx4vKamxuklWE6bZDCtybQe0CYp7GgSObRbW1sBs4Z2U1OT00uwnDbJYFqTaT2gTVLY0eT3od3Y2EhOTg6FhYWWbXPgMKYmHREtKirK6SVYTptkMK3JtB7QJimsbCosLCQnJ4fGxsZBp/v9ozlzcnLYt2+fpdtsaGhgypQpHDhwgNWrV1u6baf09vYSEhLi9DIspU0ymNZkWg9okxT+aHp0hop8ePyTTz4BoKury+GVWKekpMTpJVhOm2Qwrcm0HtAmKexoEjm0Q0NDAbOGtlJKKfUkIof2j3/8YwA6OzsdXol1UlNTnV6C5bRJBtOaTOsBbZLCjiaRQ3v8+PGEhIQYNbSjo6OdXoLltEkG05pM6wFtksKOJpFDu6KigvDwcKOGdkVFhdNLsJw2yWBak2k9oE1S2NEkcmgDxg1tpZRS6klEDu3s7GzjhnZ2drbTS7CcNslgWpNpPaBNUtjRJHJo19XVGTe06+rqnF6C5bRJBtOaTOsBbZLCjiaRQ/vatWvGDe1r1645vQTLaZMMpjWZ1gPaJIUdTcMO7e3bt1NaWsqWLVtoa2sb8jw/+9nPGDduHOnp6cOexx8iIiKMG9oRERFOL8Fy2iSDaU2m9YA2SWFH05CHMa2pqWHHjh3s2LGDtrY28vLyKCsrG3Se0tJSsrKyiImJYcWKFSQnJ7Njx47HLsAfhzEFWLp0KcnJyfzxj3+0fNtKKaVUIHiqw5gWFRWRnp4OQExMDFVVVY/tSQ8MbHiwx22nwsJC4/a0rfxAlUChTTKY1mRaD2iTFHY0DTm0PR4PsbGxvq9jY2PxeDyDzjMwsAEqKyvJy8vzzwqHYdrQVkoppZ7Eso8jycrKGvL0S5cu+fbaX3zxRVJTU5k/fz7x8fEsWrSIAwcOAJCWlkZ/fz9nz54F4K233qK8vJxbt24RGxvLsmXL2Lt3LwDBwcH09PRw/fp1CgsLWb16NVVVVdy4cYOxY8eSnZ1NaWkpALNnzyYyMpLPP/8cgJUrV1JbW0tjYyNjxoxhzZo1FBUVATBz5kzi4uI4deqUr6m+vp6GhgbCwsLYsGEDRUVF9Pf3k5KSQmJiIidOnAAgMzOThoYGPB4PwcHBbNy4kV27dtHd3c2UKVNISUnh6NGjAGRkZNDc3Ex9fT0ABQUFvpZJkyaRmprK4cOHAVi8eDHt7e2+VyXm5eXx6aefcufOHSZMmEBaWhoHDx4EYOHChXR2dnLu3DkA1q9fz/Hjx2ltbSUuLo7Fixezf/9+AObPnw/AF198AcDatWupqKjg5s2bjBs3jszMTPbs2QPAyy+/THh4OJWVlQCsWrWKmpoampqaiIqK4o033vAdKD81NZXo6GgqKirweDzcunWLuro6rl27RkREBOvWrfP9Njpjxgzi4+MpLy8H4LXXXsPtdnPlyhVCQ0N5++23KS4upq+vj+TkZJKSkjh+/DgAy5Yto7GxEbfbjcvlIj8/n927d9PV1UVSUhIzZszgyJEjALz66qvcvHmTr7/+GoD8/HwOHDjA3bt3SUxMZM6cORw6dAiAV155hY6ODs6fPw9Abm4uhw8f5vbt2yQkJDBx4kTf+tPT0+np6eGrr74CYN26dZw4cYKWlhbGjx9PRkYGH330EQDz5s3D5XJRU1MDwJo1azhz5gzNzc1ER0fz+uuvs3v3bgDmzJnD6NGjOXPmDABvvvkmX375JdevXycyMpJVq1ZRXFwMwKxZs4iNjeX06dMArFixggsXLnD16lXCw8NZv349H374IV6vl+nTpzNhwgROnjwJwPLly7l8+TIej4eSkhLy8vIoKSmht7eXadOmMXXqVI4dOwY8eDqqqamJixcvEhQUxDvvvMOePXvo7Oxk8uTJzJo1y/cU2pIlS2hpaeHChQsAbNy4kYMHD9LR0cHEiROZO3eu70N/Fi1axL1796itrQVgw4YNfPbZZ7S3tz/zfYTH4+H8+fOMGjWK6upqAPH3ETNmzGDv3r3cv3/fmPsIj8dDYWEh2dnZxtxHtLe3c/v2bT7++ONnvo94//33OX36NAkJCbS1tT32GRtDPqe9ZcsWUlJS2LRpEwDjxo2jtbX10bMBsHPnTt/5huKP57SvXr3Kv/zLv1BfX++78Uh39epVJk+e7PQyLKVNMpjWZFoPaJMU/mh6que08/Pzfb+htrW1sWDBAt/3Hn5uu7S0lI0bN/pOt+sV5OXl5URFRXHnzh1bLs8OA79JmkSbZDCtybQe0CYp7Gga8uHxtLQ0UlJSKC0tpbKyctCrwtPT03G73ZSWlvLuu+/6nvuOiYnxDXo7REZGGjW0lVJKqScZ8uFxK/nj4fEbN27whz/8gffee4+bN29aum2n3Lhxg4SEBKeXYSltksG0JtN6QJuk8EfTUz08HujcbrdxD4+73W6nl2A5bZLBtCbTekCbpLCjSeTQvnLlClFRUXR3d9Pd3e30cixx5coVp5dgOW2SwbQm03pAm6Swo0nk0A4NDSUqKgrAmL3t0NBQp5dgOW2SwbQm03pAm6Swo0nkc9oAZWVlZGdn88033zB16lTLt6+UUko5zYjntIuLi4mMjASgo6PD4dVYY+BgGSbRJhlMazKtB7RJCjuaRA7tvr4+4x4e7+vrc3oJltMmGUxrMq0HtEkKO5pEDu3k5GTjhnZycrLTS7CcNslgWpNpPaBNUtjRZNmxx+2UlJREWFgYYM7QTkpKcnoJltMmGUxrMq0HtEkKO5pE7mkfP36csWPHAtDe3u7waqwxcJB7k2iTDKY1mdYD2iSFHU1+H9qNjY3k5ORY/jmjISEhxMTEGHNENKWUUmpAYWEhOTk5NDY2Djrd7w+PJyYmWv6Wr2XLlgEQFxdnzNAeaDKJNslgWpNpPaBNUljZVFBQQEFBATk5OYNOF/nw+MBvHnFxcXz33XcOr8Yaj/42ZQJtksG0JtN6QJuksKNJ5NAeOL7rCy+8YMyeth6HVwZtCnym9YA2SaHHHh+Gy/Vg2SbtaQ80mUSbZDCtybQe0CYp7GgSexhTgC1btrBr1y4uXbrkl+0rpZRSTjLiMKa7d+8GzNrTHmgyiTbJYFqTaT2gTVLY0SRyaHd1dQGQkJDA7du3uX//vsMren4DTSbRJhlMazKtB7RJCjuaRA7tgaPOTJ48GYCrV686uRxL6NGBZNCmwGdaD2iTFHpEtGHMmDED+H//gxoaGpxcjiUGmkyiTTKY1mRaD2iTFHY0iRzaR44cAWDSpEmAGXvaA00m0SYZTGsyrQe0SQo7mkQO7QFhYWEkJCQYsaetlFJKPYnIof3qq6/6/p6UlMTly5edW4xFHm4yhTbJYFqTaT2gTVLY0SRyaD98FLSZM2fy9ddfO7gaa5hyZLeHaZMMpjWZ1gPaJIUdTSI/5evhIZ2amsr58+fx8zFi/M6EXzwepU0ymNZkWg9okxRWNg33KV9+H9oDn/JVUFDgl+3Pnj2b27dvc/36db9sXymllLJbQUEB+/btIzExcdDpIg9j2t/f7zvGq9vt5qWXXuLQoUNkZ2dbejl2erjJFNokg2lNpvWANknhjyYjDmN64MAB39+nTp1KREQEtbW1Dq7o+T3cZAptksG0JtN6QJuksKNJ5NC+e/eu7+/BwcGkpaXx+eefO7ii5/dwkym0SQbTmkzrAW2Swo4mkUP70cf4MzIyOHnypOgXoz3aZAJtksG0JtN6QJuksKNJ5NCeM2fOoK8zMjL49ttv+eabbxxa0fN7tMkE2iSDaU2m9YA2SWFHk8ihfejQoUFfv/rqq7hcLj777DOHVvT8Hm0ygTbJYFqTaT2gTVLY0SRyaD9q3LhxLF26lL179zq9FKWUUspvRA7tV1555bHT1q9fz5EjR7h9+7YDK3p+QzVJp00ymNZkWg9okxR2NIkc2h0dHY+d9vbbb9Pb28uf/vQnB1b0/IZqkk6bZDCtybQe0CYp7GgSObTPnz//2GmTJk1i3bp1vP/++yJfRT5Uk3TaJINpTab1gDZJYUeTyKE9nF/84hfU1dXxySefOL0UpZRSynIiD2Pa09PDqFGjHjvd6/WSmZnJrVu3OHv2LCEhIZZerj8N1ySZNslgWpNpPaBNUvijyfbDmPrjU74OHz485OlBQUG899571NXV8V//9V+WXZ4dhmuSTJtkMK3JtB7QJimsbDLqU76+7xXiCxYs4J/+6Z/453/+Z86cOWPZZfqb1Fe9fx9tksG0JtN6QJuksLJpuE/5EvmcdkJCwvd+/9///d9ZsGABa9as4eLFizat6vk8qUkibZLBtCbTekCbpLCjSeTQXrBgwfd+PywsjP379zN+/HiWLl1KVVWVTSt7dk9qkkibZDCtybQe0CYp7GgSObQ//vjjJ55n/PjxnDhxgmnTprFs2TJ+//vf09/fb8Pqns3TNEmjTTKY1mRaD2iTFHY0iRzaT+uFF17g6NGj/PSnP+WXv/wlS5cupby83OllKaWUUs9E5NBOT09/6vNGRETw+9//nqNHj3Lv3j2WLl3Ka6+9RklJCd3d3X5c5ciMpEkKbZLBtCbTekCbpLCjSeTQ7unpGfG/Wb58OdXV1RQXF9PT08PGjRtJTEzk7/7u79i7d6/jH8j+LE2BTptkMK3JtB7QJinsaBI5tL/66qtn+ncul4u8vDxOnjxJbW0tP/3pT/nLX/7C+vXriY6OZtGiRfzDP/wDxcXF/PWvf6W3t9filQ/vWZsCmTbJYFqTaT2gTVLY0STnkGEWmz17Nr/73e/43e9+x8WLFzl69Cjl5eXs3buX//7v/wYgNDSUH/3oR6SmpjJt2jSmTZvG1KlTmTZtGpMnTyY0NNTZCKWUUj8oIg9jev/+fSIiIizd5sO+++47amtrqa2t5dy5c1y4cIHLly9z7dq1QR9GEhsbS0JCwmN/xo8fT0xMzGN/oqOjCQ8Pd6TJCdokg2lNpvWANknhj6ZHZ+iwe9rbt28nOTmZyspKtm7dSkxMzDOdxx9OnDjBypUr/bb9F154geXLl7N8+fJBp3d3d3P16lUuX77MlStXaG5u5saNG74/tbW13Lhxg5aWlmHfXhYWFkZ0dDRjxoxhzJgxjB49mjFjxnD37l2mTp066LSB/4aHhxMWFkZoaChhYWGD/jzNaaNGjSIoKMhv/7+G4+/ryQnaFPhM6wFtksKOpiGHdk1NDW63m82bN5OVlUVeXh5lZWUjPo+/tLS02HI5jwoNDSUlJYWUlJTvPZ/X66Wjo4O2trYh/7S3t3Pv3j3u3r3L3bt3uXfvHvX19bS0tHD16tVB37t79y5dXV10dXU919pdLhchISGMGjWKkJCQx/4MdfrTnDc4OJjg4GBcLpfvvwN/v3jxIocOHRrye0P99/u+N5J/HxQURFBQEC6Xa8j/Puv3XC4X586dY+rUqZZv+/u+529O3Z78xbQe0CYp7GgacmgXFRX5XroeExNDVVUVbW1tg/akn+Y8/jJ+/Hi/X8bzCAoKIioqiqioKCZPnvxU/+bw4cNkZ2cP+32v10tPTw9dXV10d3f7Bvmjf4b6Xm9vL729vfT09Pj+PtSfp/3+/fv3B53e399PX1/foP/29/fT3t7O1atXH/vecP/9vu8Fkl//+te2Xt7AUH/S0P++P49u5+HTu7u7+fWvf/3U/+ZJ27Pj33zf927dusUf//hHR9f36OkPX5fP8verV69SXl5uybae5e/+2K7H46Gurs62y/u+v4/kfE4bcmh7PB4WLlzo+zo2NhaPx0NaWtqIzgNw6dIl33B/8cUXSU1NZf78+cTHx7No0SIOHDgAQFpaGv39/Zw9exaAt956i/Lycm7dukVsbCzLli1j7969AEyfPp36+nqqq6sBWL16NVVVVdy4cYOxY8eSnZ1NaWkp8OAFZ5GRkXz++ecArFy5ktraWhobGxkzZgxr1qyhqKgIgJkzZxIXF8epU6cAyMrKor6+noaGBsLCwtiwYQNFRUX09/eTkpJCYmIiJ06cACAzM5OGhgY8Hg/BwcFs3LiRXbt20d3dzZQpU0hJSeHo0aMAZGRk0NzcTH19PfDgwPCtra0UFhYyadIkUlNTfZ8Ws3jxYtrb230/3Hl5efz5z3/mzp07TJgwgbS0NA4ePAjAwoUL6ezs5Ny5cwCsX7+e48eP09raSlxcHIsXL2b//v0AzJ8/H4AvvvgCgLVr11JRUcHNmzcZN24cmZmZ7NmzB4CXX36Z8PBwKisrAVi1ahU1NTU0NTURFRXFG2+8QUlJCQCpqalER0dTUVFBV1cXa9eupa6ujmvXrhEREcG6det8n/g2Y8YM4uPjfQe8ee2113C73Vy5coXQ0FDefvttiouL6evrY+rUqUyaNIljx47h9XpZvHgx165dw+Px+H5e9u3bR2dnJxMnTmTatGmcPHkSr9dLWloat27dwuPx4PV6efPNNzl27Bh3794lPj6elJQUTp06RX9/P3PmzOHevXtcunQJr9fL8uXL+ctf/kJHRwfjxo0jKSmJmpoaAF566SV6e3u5dOkSAH/zN3/DuXPnuH37NpGRkaSmplJRUYHX62Xq1Km+201/fz9paWlcvHiR9vZ2wsPDSU1NpbKyEq/XS2JiIiEhIb71zp49m4aGBtra2ggLC+NHP/oRNTU19Pf3Ex8fT0REBN988w1er5fp06dz48YN2traCAkJYfbs2Zw9exav18v48eMZM2YMly9fBmDatGm0trby3XffMWrUKH70ox9x/vx5+vv7iYmJYezYsb7zTpo0iY6ODlpbW/F6vcyaNYv6+np6e3uJiopi3LhxXLlyBa/Xy4QJE+js7PSd96WXXuLy5ct0d3czevRoYmNjuXr1Kl6vlxdeeIGenh7fHsqUKVO4fv06XV1dhIeHM378eN95x40bR39/v++8EydO5ObNm3R2dhIaGkpcXBzXrl0jKCiIvr4+XC6Xbw0JCQm0tLTQ2dlJSEgIcXFxfPvtt3i9XiIjI3G5XLS3t/v+P925c4fOzk5cLhcvvPACTU1NeL1eIiIiGDVqFG1tbcCDHZZ79+7R2dlJUFAQsbGx3Lx5E6/X63tq6s6dOwBERUXR3d1NZ2en7z6ztbWV/v5+31NaAx84ERkZ6fslHSA6Opra2tpB5x3YbkREBP39/XR2duL1eomOjqajo4O+vj5CQkKIiIjwbffh8w6s6e7du/T39+NyuYiIiKCjowOv1+t7/c3AeceMGUNnZye9vb0EBwcTERHhW0NYWBiAb70RERF0dXXR19dHUFAQo0ePpqOjA4BRo0bhcrm4f/8+QUFBRERE0N3d7TtvRESE7y24ISEhuFwu33bDwsLo7e2lr68PgPDwcO7fv4/X6/U98jdw3tDQ0EHnDQsLo6urC6/X63vkceBYHSEhIb6dhIHrrru723fe4OBg39u5Hj7vwOX09PTg9XpZvXo1t2/f9h0ZLT09nZ6eHt+rytetW8eJEydoaWlh/PjxZGRk8NFHHwEwb948XC4X77//PqdPnyYhIYG2trbHH2X1DiE3N9dbUlLi+zo5OdlbXV094vN4vV7v2rVrh7qI5/LBBx9Yvk2naZMM2hT4TOvxerVJCn80PTpDh3yfdnJy8qDH5ltaWh7bg36a8yillFLKOkMO7fz8fN9Dz21tbYM+uWTgYaHvO4+/zZs3z7bLsos2yaBNgc+0HtAmKexoGvI57bS0NFJSUigtLaWyspIdO3b4vpeeno7b7f7e8/ibyyXyQG7fS5tk0KbAZ1oPaJMUdjQNewmbN28mNzeXbdu2kZyc7Dvd7XY/8Tz+NvBCIJNokwzaFPhM6wFtksKOJvN+1VFKKaUMJfIwpnfu3CEqKsrSbTpNm2TQpsBnWg9okxT+aHp0horc0z5z5ozTS7CcNsmgTYHPtB7QJinsaBI5tJubm51eguW0SQZtCnym9YA2SWFHk8ihPXAUL5NokwzaFPhM6wFtksKOJpFD++Hj1ZpCm2TQpsBnWg9okxR2NAXU0B44JvWTfPvtt5Zv82nP569t/pCb/LHOkZxXSpO//j+Z1uSP29JIziulyenbnZQmp+/HHyVyaPtjm05f2SNhWpPTdx5Smvz1/8kf29Qm6y/f6m06fbsbCdPu855nu35/y9fs2bOf+PnTAxobG0lMTLTsfCZu0+nLl7JNpy9fyjadvnwp23T68qVs0+nLl7LNkZzX7XZz/vx539d+H9pKKaWUskZAPTyulFJKqeHp0FZKKaWE0KEdYAY++lQiyWsfiR9KpykkX1+S1z5SP6TW5yFqaG/fvp3S0lK2bNki8gr+2c9+xrhx40hPTx+0/iNHjpCSkkJKSgo7d+70nS6hd6RrD+Sm0tJSxo0b5+t5+AWU0q4jj8dDXl4eR44cGXT6SK6XQGobrmckt6lA6oHhm0bysyahaaS3q0BrguF/zhy5PXmFqK6u9m7atMnr9Xq9ra2t3qysLIdXNDIlJSXe1tZWr9fr9WZlZflavF6vd9u2bY+dX0rvSNYe6E1lZWW+v7vdbu/mzZt9X0u8jnJzcwc1jeR6CcS2R3tGcpsKxB6v9/Emr/fpf9akNI3kdhWITcP9nDl1exKzp11UVER6ejoAMTExVFVVBcxvYU8jKyuLmJgY4MFvbQM8Hg9btmwhJSWF0tJS3+kSeke69kBvysrK8v39yJEjrFixApB9HT1sJNfLH//4x4BvG8ltSsp1NZKfNQnXEYzsdhWI19NwP2dO3Z7EDG2Px0NsbKzv69jYWDwej4MrGpmBKx2gsrKSvLw8AJKTk2ltbWXbtm28++67voeVJPSOdO0SmgaUlZX57mwkX0cPG8n1UltbG/BtI7lNSbmuRvKzJuE6etSTbleBeD0N93Pm1O0p5Jn+lXpuD//2GRMTQ25uLi0tLezYsWPQ9wKd5LWPxA+lU7Lvu01J8kP6WZN4PTl9fYjZ005OTqalpcX3dUtLC2lpaQ6u6Nns3LmTbdu2Dfm9h38YpPU+zdqlNJWWlpKfnz/k9yRfRyO5XhYsWCCm7WluU9KuK3jyz5qk6wie7nYVyNfToz9nTt2exAzt/Px8qqurgQdvDViwYIHDKxq50tJSNm7cCDxoePQ5jZqaGt8PtbTep1m7lKaioqJhf5uWfB2N5HqR0va0tykpPQ970s+atKanuV0FatNQP2dO3Z7EPDyelpbme8FCZWWlmIdSBpSWlvLuu+/6nteIiYmhurqanTt3smPHDrZu3QpAbm4uIKN3pGuX0AQPblQPP48l8TryeDzU1NRQVlbGggULiImJGdH1kpycHFBtQ/WM9DYVSD0wdNNIftYC7TqCoZsGPO3tKtCahvs5c+r2pMceV0oppYQQ8/C4Ukop9UOnQ1sppZQSQoe2UkopJYQObaWUUkoIHdpKKaWUEDq0lVJKKSH+P9t6hKI4zx9hAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use([\"science\", \"ieee\", \"grid\"])\n",
    "plt.subplots(figsize=(6, 4), dpi=100)\n",
    "plt.subplot(1, 1, 1)\n",
    "plt.plot(list(range(len(posterior_mean_coef1))), posterior_mean_coef1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3441484/3495330613.py:2: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  posterior_mean_coef2 = torch.tensor(posterior_mean_coef2, dtype=torch.float32)\n"
     ]
    }
   ],
   "source": [
    "posterior_mean_coef2 = (1.0 - gammas_prev) * np.sqrt(alphas) / (1.0 - gammas)\n",
    "posterior_mean_coef2 = torch.tensor(posterior_mean_coef2, dtype=torch.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbcaca440a0>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use([\"science\", \"ieee\", \"grid\"])\n",
    "plt.subplots(figsize=(6, 4), dpi=100)\n",
    "plt.subplot(1, 1, 1)\n",
    "plt.plot(list(range(len(posterior_mean_coef2))), posterior_mean_coef2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from inspect import isfunction\n",
    "\n",
    "def exists(x):\n",
    "    return x is not None\n",
    "\n",
    "\n",
    "def default(val, d):\n",
    "    if exists(val):\n",
    "        return val\n",
    "    return d() if isfunction(d) else d\n",
    "\n",
    "\n",
    "def extract(a, t, x_shape=(1, 1, 1, 1)):\n",
    "    b, *_ = t.shape\n",
    "    out = a.gather(-1, t)\n",
    "    return out.reshape(b, *((1,) * (len(x_shape) - 1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_start_from_noise(y_t, t, noise):\n",
    "    return (\n",
    "        extract(sqrt_recip_gammas, t, y_t.shape) * y_t\n",
    "        - extract(sqrt_recipm1_gammas, t, y_t.shape) * noise\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def q_posterior(y_0_hat, y_t, t):\n",
    "    posterior_mean = (\n",
    "        extract(posterior_mean_coef1, t, y_t.shape) * y_0_hat\n",
    "        + extract(posterior_mean_coef2, t, y_t.shape) * y_t\n",
    "    )\n",
    "    p = extract(\n",
    "        posterior_log_variance_clipped, t, y_t.shape\n",
    "    )\n",
    "    return posterior_mean, p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def p_mean_variance(y_t, t, clip_denoised: bool, y_cond=None):\n",
    "    noise_level = extract(gammas, t, x_shape=(1, 1)).to(y_t.device)\n",
    "    y_0_hat = predict_start_from_noise(\n",
    "        y_t,\n",
    "        t=t,\n",
    "        noise=denoise_fn(torch.cat([y_cond, y_t], dim=1), noise_level),\n",
    "    )\n",
    "\n",
    "    if clip_denoised:\n",
    "        y_0_hat.clamp_(-1.0, 1.0)\n",
    "\n",
    "    model_mean, posterior_log_variance = q_posterior(\n",
    "        y_0_hat=y_0_hat, y_t=y_t, t=t\n",
    "    )\n",
    "    return model_mean, posterior_log_variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def q_sample(y_0, sample_gammas, noise=None):\n",
    "    noise = default(noise, lambda: torch.randn_like(y_0))\n",
    "    return sample_gammas.sqrt() * y_0 + (1 - sample_gammas).sqrt() * noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "@torch.no_grad()\n",
    "def p_sample(y_t, t, clip_denoised=True, y_cond=None):\n",
    "    model_mean, model_log_variance = p_mean_variance(\n",
    "        y_t=y_t, t=t, clip_denoised=clip_denoised, y_cond=y_cond\n",
    "    )\n",
    "    noise = torch.randn_like(y_t) if any(t > 0) else torch.zeros_like(y_t)\n",
    "    return model_mean + noise * (0.5 * model_log_variance).exp()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "@torch.no_grad()\n",
    "def restoration(y_cond, y_t=None, y_0=None, mask=None, sample_num=8):\n",
    "    b, *_ = y_cond.shape\n",
    "\n",
    "    assert (\n",
    "        timesteps > sample_num\n",
    "    ), \"num_timesteps must greater than sample_num\"\n",
    "    sample_inter = timesteps // sample_num\n",
    "\n",
    "    y_t = default(y_t, lambda: torch.randn_like(y_cond))\n",
    "    ret_arr = y_t\n",
    "    for i in tqdm(\n",
    "        reversed(range(timesteps)),\n",
    "        desc=\"sampling loop time step\",\n",
    "        total=timesteps,\n",
    "    ):\n",
    "        t = torch.full((b,), i, device=y_cond.device, dtype=torch.long)\n",
    "        y_t = p_sample(y_t, t, y_cond=y_cond)\n",
    "        if mask is not None:\n",
    "            y_t = y_0 * (1.0 - mask) + mask * y_t\n",
    "        if i % sample_inter == 0:\n",
    "            ret_arr = torch.cat([ret_arr, y_t], dim=0)\n",
    "    return y_t, ret_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def forward(y_0, y_cond=None, mask=None, noise=None):\n",
    "    b, *_ = y_0.shape\n",
    "    t = torch.randint(1, timesteps, (b,), device=y_0.device).long()\n",
    "    gamma_t1 = extract(gammas, t - 1, x_shape=(1, 1))\n",
    "    sqrt_gamma_t2 = extract(gammas, t, x_shape=(1, 1))\n",
    "    sample_gammas = (sqrt_gamma_t2 - gamma_t1) * torch.rand(\n",
    "        (b, 1), device=y_0.device\n",
    "    ) + gamma_t1\n",
    "    sample_gammas = sample_gammas.view(b, -1)\n",
    "\n",
    "    noise = default(noise, lambda: torch.randn_like(y_0))\n",
    "    y_noisy = q_sample(\n",
    "        y_0=y_0, sample_gammas=sample_gammas.view(-1, 1, 1, 1), noise=noise\n",
    "    )\n",
    "\n",
    "    if mask is not None:\n",
    "        noise_hat = denoise_fn(\n",
    "            torch.cat([y_cond, y_noisy * mask + (1.0 - mask) * y_0], dim=1),\n",
    "            sample_gammas,\n",
    "        )\n",
    "        return mask * noise, mask * noise_hat\n",
    "    else:\n",
    "        noise_hat = denoise_fn(\n",
    "            torch.cat([y_cond, y_noisy], dim=1), sample_gammas\n",
    "        )\n",
    "        return noise, noise_hat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3441484/2783977364.py:1: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  gt_image = torch.tensor(single_data[\"gt_image\"], dtype=torch.float32).unsqueeze(0)\n",
      "/tmp/ipykernel_3441484/2783977364.py:2: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  cond_image = torch.tensor(single_data[\"cond_image\"], dtype=torch.float32).unsqueeze(0)\n",
      "/tmp/ipykernel_3441484/2783977364.py:3: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  mask = torch.tensor(single_data[\"mask\"], dtype=torch.float32).unsqueeze(0)\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fbcaa1df7f0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gt_image = torch.tensor(single_data[\"gt_image\"], dtype=torch.float32).unsqueeze(0)\n",
    "cond_image = torch.tensor(single_data[\"cond_image\"], dtype=torch.float32).unsqueeze(0)\n",
    "mask = torch.tensor(single_data[\"mask\"], dtype=torch.float32).unsqueeze(0)\n",
    "noise, noise_hat = forward(gt_image, cond_image, mask)\n",
    "plt.subplots(1, 2, figsize=(6, 3), dpi=100)\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.imshow(noise[0].permute(1, 2, 0))\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.imshow(noise_hat[0].detach().permute(1, 2, 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "sampling loop time step: 100%|██████████| 2000/2000 [48:03<00:00,  1.44s/it]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(tensor([[[[-0.3098, -0.3098, -0.3098,  ..., -0.7569, -0.7569, -0.7569],\n",
       "           [-0.3098, -0.3098, -0.3098,  ..., -0.7647, -0.7569, -0.7569],\n",
       "           [-0.3098, -0.3098, -0.3098,  ..., -0.7647, -0.7647, -0.7647],\n",
       "           ...,\n",
       "           [-0.2784, -0.2784, -0.2784,  ...,  0.0196, -0.0431, -0.0980],\n",
       "           [-0.2784, -0.2784, -0.2784,  ...,  0.0510, -0.0039, -0.0588],\n",
       "           [-0.2863, -0.2863, -0.2863,  ...,  0.0745,  0.0196, -0.0353]],\n",
       " \n",
       "          [[-0.3961, -0.3961, -0.3961,  ..., -0.5843, -0.5843, -0.5843],\n",
       "           [-0.3961, -0.3961, -0.3961,  ..., -0.5922, -0.5843, -0.5843],\n",
       "           [-0.3961, -0.3961, -0.3961,  ..., -0.5922, -0.5922, -0.5922],\n",
       "           ...,\n",
       "           [-0.3490, -0.3490, -0.3490,  ...,  0.1137,  0.0588,  0.0196],\n",
       "           [-0.3490, -0.3490, -0.3490,  ...,  0.1451,  0.0980,  0.0588],\n",
       "           [-0.3569, -0.3569, -0.3569,  ...,  0.1686,  0.1216,  0.0824]],\n",
       " \n",
       "          [[-0.5686, -0.5686, -0.5686,  ..., -0.4039, -0.4039, -0.4039],\n",
       "           [-0.5686, -0.5686, -0.5686,  ..., -0.4118, -0.4039, -0.4039],\n",
       "           [-0.5686, -0.5686, -0.5686,  ..., -0.4118, -0.4118, -0.4118],\n",
       "           ...,\n",
       "           [-0.5765, -0.5765, -0.5765,  ...,  0.0353, -0.0118, -0.0353],\n",
       "           [-0.5765, -0.5765, -0.5765,  ...,  0.0667,  0.0275,  0.0039],\n",
       "           [-0.5843, -0.5843, -0.5843,  ...,  0.0902,  0.0510,  0.0275]]]]),\n",
       " tensor([[[[-0.3098, -0.3098, -0.3098,  ..., -0.7569, -0.7569, -0.7569],\n",
       "           [-0.3098, -0.3098, -0.3098,  ..., -0.7647, -0.7569, -0.7569],\n",
       "           [-0.3098, -0.3098, -0.3098,  ..., -0.7647, -0.7647, -0.7647],\n",
       "           ...,\n",
       "           [-0.2784, -0.2784, -0.2784,  ...,  0.0196, -0.0431, -0.0980],\n",
       "           [-0.2784, -0.2784, -0.2784,  ...,  0.0510, -0.0039, -0.0588],\n",
       "           [-0.2863, -0.2863, -0.2863,  ...,  0.0745,  0.0196, -0.0353]],\n",
       " \n",
       "          [[-0.3961, -0.3961, -0.3961,  ..., -0.5843, -0.5843, -0.5843],\n",
       "           [-0.3961, -0.3961, -0.3961,  ..., -0.5922, -0.5843, -0.5843],\n",
       "           [-0.3961, -0.3961, -0.3961,  ..., -0.5922, -0.5922, -0.5922],\n",
       "           ...,\n",
       "           [-0.3490, -0.3490, -0.3490,  ...,  0.1137,  0.0588,  0.0196],\n",
       "           [-0.3490, -0.3490, -0.3490,  ...,  0.1451,  0.0980,  0.0588],\n",
       "           [-0.3569, -0.3569, -0.3569,  ...,  0.1686,  0.1216,  0.0824]],\n",
       " \n",
       "          [[-0.5686, -0.5686, -0.5686,  ..., -0.4039, -0.4039, -0.4039],\n",
       "           [-0.5686, -0.5686, -0.5686,  ..., -0.4118, -0.4039, -0.4039],\n",
       "           [-0.5686, -0.5686, -0.5686,  ..., -0.4118, -0.4118, -0.4118],\n",
       "           ...,\n",
       "           [-0.5765, -0.5765, -0.5765,  ...,  0.0353, -0.0118, -0.0353],\n",
       "           [-0.5765, -0.5765, -0.5765,  ...,  0.0667,  0.0275,  0.0039],\n",
       "           [-0.5843, -0.5843, -0.5843,  ...,  0.0902,  0.0510,  0.0275]]],\n",
       " \n",
       " \n",
       "         [[[-0.3098, -0.3098, -0.3098,  ..., -0.7569, -0.7569, -0.7569],\n",
       "           [-0.3098, -0.3098, -0.3098,  ..., -0.7647, -0.7569, -0.7569],\n",
       "           [-0.3098, -0.3098, -0.3098,  ..., -0.7647, -0.7647, -0.7647],\n",
       "           ...,\n",
       "           [-0.2784, -0.2784, -0.2784,  ...,  0.0196, -0.0431, -0.0980],\n",
       "           [-0.2784, -0.2784, -0.2784,  ...,  0.0510, -0.0039, -0.0588],\n",
       "           [-0.2863, -0.2863, -0.2863,  ...,  0.0745,  0.0196, -0.0353]],\n",
       " \n",
       "          [[-0.3961, -0.3961, -0.3961,  ..., -0.5843, -0.5843, -0.5843],\n",
       "           [-0.3961, -0.3961, -0.3961,  ..., -0.5922, -0.5843, -0.5843],\n",
       "           [-0.3961, -0.3961, -0.3961,  ..., -0.5922, -0.5922, -0.5922],\n",
       "           ...,\n",
       "           [-0.3490, -0.3490, -0.3490,  ...,  0.1137,  0.0588,  0.0196],\n",
       "           [-0.3490, -0.3490, -0.3490,  ...,  0.1451,  0.0980,  0.0588],\n",
       "           [-0.3569, -0.3569, -0.3569,  ...,  0.1686,  0.1216,  0.0824]],\n",
       " \n",
       "          [[-0.5686, -0.5686, -0.5686,  ..., -0.4039, -0.4039, -0.4039],\n",
       "           [-0.5686, -0.5686, -0.5686,  ..., -0.4118, -0.4039, -0.4039],\n",
       "           [-0.5686, -0.5686, -0.5686,  ..., -0.4118, -0.4118, -0.4118],\n",
       "           ...,\n",
       "           [-0.5765, -0.5765, -0.5765,  ...,  0.0353, -0.0118, -0.0353],\n",
       "           [-0.5765, -0.5765, -0.5765,  ...,  0.0667,  0.0275,  0.0039],\n",
       "           [-0.5843, -0.5843, -0.5843,  ...,  0.0902,  0.0510,  0.0275]]],\n",
       " \n",
       " \n",
       "         [[[-0.3098, -0.3098, -0.3098,  ..., -0.7569, -0.7569, -0.7569],\n",
       "           [-0.3098, -0.3098, -0.3098,  ..., -0.7647, -0.7569, -0.7569],\n",
       "           [-0.3098, -0.3098, -0.3098,  ..., -0.7647, -0.7647, -0.7647],\n",
       "           ...,\n",
       "           [-0.2784, -0.2784, -0.2784,  ...,  0.0196, -0.0431, -0.0980],\n",
       "           [-0.2784, -0.2784, -0.2784,  ...,  0.0510, -0.0039, -0.0588],\n",
       "           [-0.2863, -0.2863, -0.2863,  ...,  0.0745,  0.0196, -0.0353]],\n",
       " \n",
       "          [[-0.3961, -0.3961, -0.3961,  ..., -0.5843, -0.5843, -0.5843],\n",
       "           [-0.3961, -0.3961, -0.3961,  ..., -0.5922, -0.5843, -0.5843],\n",
       "           [-0.3961, -0.3961, -0.3961,  ..., -0.5922, -0.5922, -0.5922],\n",
       "           ...,\n",
       "           [-0.3490, -0.3490, -0.3490,  ...,  0.1137,  0.0588,  0.0196],\n",
       "           [-0.3490, -0.3490, -0.3490,  ...,  0.1451,  0.0980,  0.0588],\n",
       "           [-0.3569, -0.3569, -0.3569,  ...,  0.1686,  0.1216,  0.0824]],\n",
       " \n",
       "          [[-0.5686, -0.5686, -0.5686,  ..., -0.4039, -0.4039, -0.4039],\n",
       "           [-0.5686, -0.5686, -0.5686,  ..., -0.4118, -0.4039, -0.4039],\n",
       "           [-0.5686, -0.5686, -0.5686,  ..., -0.4118, -0.4118, -0.4118],\n",
       "           ...,\n",
       "           [-0.5765, -0.5765, -0.5765,  ...,  0.0353, -0.0118, -0.0353],\n",
       "           [-0.5765, -0.5765, -0.5765,  ...,  0.0667,  0.0275,  0.0039],\n",
       "           [-0.5843, -0.5843, -0.5843,  ...,  0.0902,  0.0510,  0.0275]]],\n",
       " \n",
       " \n",
       "         ...,\n",
       " \n",
       " \n",
       "         [[[-0.3098, -0.3098, -0.3098,  ..., -0.7569, -0.7569, -0.7569],\n",
       "           [-0.3098, -0.3098, -0.3098,  ..., -0.7647, -0.7569, -0.7569],\n",
       "           [-0.3098, -0.3098, -0.3098,  ..., -0.7647, -0.7647, -0.7647],\n",
       "           ...,\n",
       "           [-0.2784, -0.2784, -0.2784,  ...,  0.0196, -0.0431, -0.0980],\n",
       "           [-0.2784, -0.2784, -0.2784,  ...,  0.0510, -0.0039, -0.0588],\n",
       "           [-0.2863, -0.2863, -0.2863,  ...,  0.0745,  0.0196, -0.0353]],\n",
       " \n",
       "          [[-0.3961, -0.3961, -0.3961,  ..., -0.5843, -0.5843, -0.5843],\n",
       "           [-0.3961, -0.3961, -0.3961,  ..., -0.5922, -0.5843, -0.5843],\n",
       "           [-0.3961, -0.3961, -0.3961,  ..., -0.5922, -0.5922, -0.5922],\n",
       "           ...,\n",
       "           [-0.3490, -0.3490, -0.3490,  ...,  0.1137,  0.0588,  0.0196],\n",
       "           [-0.3490, -0.3490, -0.3490,  ...,  0.1451,  0.0980,  0.0588],\n",
       "           [-0.3569, -0.3569, -0.3569,  ...,  0.1686,  0.1216,  0.0824]],\n",
       " \n",
       "          [[-0.5686, -0.5686, -0.5686,  ..., -0.4039, -0.4039, -0.4039],\n",
       "           [-0.5686, -0.5686, -0.5686,  ..., -0.4118, -0.4039, -0.4039],\n",
       "           [-0.5686, -0.5686, -0.5686,  ..., -0.4118, -0.4118, -0.4118],\n",
       "           ...,\n",
       "           [-0.5765, -0.5765, -0.5765,  ...,  0.0353, -0.0118, -0.0353],\n",
       "           [-0.5765, -0.5765, -0.5765,  ...,  0.0667,  0.0275,  0.0039],\n",
       "           [-0.5843, -0.5843, -0.5843,  ...,  0.0902,  0.0510,  0.0275]]],\n",
       " \n",
       " \n",
       "         [[[-0.3098, -0.3098, -0.3098,  ..., -0.7569, -0.7569, -0.7569],\n",
       "           [-0.3098, -0.3098, -0.3098,  ..., -0.7647, -0.7569, -0.7569],\n",
       "           [-0.3098, -0.3098, -0.3098,  ..., -0.7647, -0.7647, -0.7647],\n",
       "           ...,\n",
       "           [-0.2784, -0.2784, -0.2784,  ...,  0.0196, -0.0431, -0.0980],\n",
       "           [-0.2784, -0.2784, -0.2784,  ...,  0.0510, -0.0039, -0.0588],\n",
       "           [-0.2863, -0.2863, -0.2863,  ...,  0.0745,  0.0196, -0.0353]],\n",
       " \n",
       "          [[-0.3961, -0.3961, -0.3961,  ..., -0.5843, -0.5843, -0.5843],\n",
       "           [-0.3961, -0.3961, -0.3961,  ..., -0.5922, -0.5843, -0.5843],\n",
       "           [-0.3961, -0.3961, -0.3961,  ..., -0.5922, -0.5922, -0.5922],\n",
       "           ...,\n",
       "           [-0.3490, -0.3490, -0.3490,  ...,  0.1137,  0.0588,  0.0196],\n",
       "           [-0.3490, -0.3490, -0.3490,  ...,  0.1451,  0.0980,  0.0588],\n",
       "           [-0.3569, -0.3569, -0.3569,  ...,  0.1686,  0.1216,  0.0824]],\n",
       " \n",
       "          [[-0.5686, -0.5686, -0.5686,  ..., -0.4039, -0.4039, -0.4039],\n",
       "           [-0.5686, -0.5686, -0.5686,  ..., -0.4118, -0.4039, -0.4039],\n",
       "           [-0.5686, -0.5686, -0.5686,  ..., -0.4118, -0.4118, -0.4118],\n",
       "           ...,\n",
       "           [-0.5765, -0.5765, -0.5765,  ...,  0.0353, -0.0118, -0.0353],\n",
       "           [-0.5765, -0.5765, -0.5765,  ...,  0.0667,  0.0275,  0.0039],\n",
       "           [-0.5843, -0.5843, -0.5843,  ...,  0.0902,  0.0510,  0.0275]]],\n",
       " \n",
       " \n",
       "         [[[-0.3098, -0.3098, -0.3098,  ..., -0.7569, -0.7569, -0.7569],\n",
       "           [-0.3098, -0.3098, -0.3098,  ..., -0.7647, -0.7569, -0.7569],\n",
       "           [-0.3098, -0.3098, -0.3098,  ..., -0.7647, -0.7647, -0.7647],\n",
       "           ...,\n",
       "           [-0.2784, -0.2784, -0.2784,  ...,  0.0196, -0.0431, -0.0980],\n",
       "           [-0.2784, -0.2784, -0.2784,  ...,  0.0510, -0.0039, -0.0588],\n",
       "           [-0.2863, -0.2863, -0.2863,  ...,  0.0745,  0.0196, -0.0353]],\n",
       " \n",
       "          [[-0.3961, -0.3961, -0.3961,  ..., -0.5843, -0.5843, -0.5843],\n",
       "           [-0.3961, -0.3961, -0.3961,  ..., -0.5922, -0.5843, -0.5843],\n",
       "           [-0.3961, -0.3961, -0.3961,  ..., -0.5922, -0.5922, -0.5922],\n",
       "           ...,\n",
       "           [-0.3490, -0.3490, -0.3490,  ...,  0.1137,  0.0588,  0.0196],\n",
       "           [-0.3490, -0.3490, -0.3490,  ...,  0.1451,  0.0980,  0.0588],\n",
       "           [-0.3569, -0.3569, -0.3569,  ...,  0.1686,  0.1216,  0.0824]],\n",
       " \n",
       "          [[-0.5686, -0.5686, -0.5686,  ..., -0.4039, -0.4039, -0.4039],\n",
       "           [-0.5686, -0.5686, -0.5686,  ..., -0.4118, -0.4039, -0.4039],\n",
       "           [-0.5686, -0.5686, -0.5686,  ..., -0.4118, -0.4118, -0.4118],\n",
       "           ...,\n",
       "           [-0.5765, -0.5765, -0.5765,  ...,  0.0353, -0.0118, -0.0353],\n",
       "           [-0.5765, -0.5765, -0.5765,  ...,  0.0667,  0.0275,  0.0039],\n",
       "           [-0.5843, -0.5843, -0.5843,  ...,  0.0902,  0.0510,  0.0275]]]]))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "restoration(cond_image, cond_image, gt_image, mask, sample_num=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global seed set to 42\n",
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "[rank: 0] Global seed set to 42\n",
      "Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/2\n",
      "[rank: 1] Global seed set to 42\n",
      "[rank: 1] Global seed set to 42\n",
      "Initializing distributed: GLOBAL_RANK: 1, MEMBER: 2/2\n",
      "----------------------------------------------------------------------------------------------------\n",
      "distributed_backend=nccl\n",
      "All distributed processes registered. Starting with 2 processes\n",
      "----------------------------------------------------------------------------------------------------\n",
      "\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n",
      "LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1]\n",
      "\n",
      "  | Name | Type    | Params\n",
      "---------------------------------\n",
      "0 | loss | MSELoss | 0     \n",
      "1 | net  | Network | 62.6 M\n",
      "---------------------------------\n",
      "62.6 M    Trainable params\n",
      "0         Non-trainable params\n",
      "62.6 M    Total params\n",
      "250.565   Total estimated model params size (MB)\n",
      "Sanity Checking DataLoader 0:   0%|                       | 0/1 [00:00<?, ?it/s]\n",
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      "\n",
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      "Validation: 0it [00:00, ?it/s]\u001b[A\n",
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      "\n",
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      "\n",
      "Epoch 0: 100%|████████████████████████| 62/62 [02:15<00:00,  2.18s/it, v_num=37]\u001b[A\n",
      "Epoch 1: 100%|████████████████████████| 62/62 [00:30<00:00,  2.05it/s, v_num=37]\u001b[A\n",
      "Validation: 0it [00:00, ?it/s]\u001b[A\n",
      "Validation:   0%|                                         | 0/1 [00:00<?, ?it/s]\u001b[A\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Process ForkProcess-39:\n",
      "Process ForkProcess-36:\n",
      "Process ForkProcess-28:\n",
      "Process ForkProcess-15:\n",
      "Process ForkProcess-34:\n",
      "Process ForkProcess-29:\n",
      "Process ForkProcess-27:\n",
      "Process ForkProcess-35:\n",
      "Process ForkProcess-30:\n",
      "Process ForkProcess-31:\n",
      "Process ForkProcess-34:\n",
      "Process ForkProcess-17:\n",
      "Process ForkProcess-35:\n",
      "Process ForkProcess-32:\n",
      "Process ForkProcess-15:\n",
      "Process ForkProcess-40:\n",
      "Process ForkProcess-14:\n",
      "Process ForkProcess-25:\n",
      "Process ForkProcess-38:\n",
      "Process ForkProcess-23:\n",
      "Process ForkProcess-28:\n",
      "Process ForkProcess-13:\n",
      "Process ForkProcess-20:\n",
      "Process ForkProcess-33:\n",
      "Process ForkProcess-10:\n",
      "Process ForkProcess-14:\n",
      "Process ForkProcess-9:\n",
      "Process ForkProcess-21:\n",
      "Process ForkProcess-9:\n",
      "Process ForkProcess-37:\n",
      "Process ForkProcess-26:\n",
      "Process ForkProcess-12:\n",
      "Process ForkProcess-23:\n",
      "Process ForkProcess-21:\n",
      "Process ForkProcess-19:\n",
      "Process ForkProcess-17:\n",
      "Process ForkProcess-25:\n",
      "Process ForkProcess-38:\n",
      "Process ForkProcess-31:\n",
      "Process ForkProcess-26:\n",
      "Process ForkProcess-39:\n",
      "Process ForkProcess-10:\n",
      "Traceback (most recent call last):\n",
      "Process ForkProcess-40:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "Process ForkProcess-24:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "Process ForkProcess-37:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "Process ForkProcess-11:\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "KeyboardInterrupt\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Process ForkProcess-18:\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Process ForkProcess-27:\n",
      "Process ForkProcess-13:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "Process ForkProcess-18:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "Process ForkProcess-12:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Process ForkProcess-29:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "Process ForkProcess-24:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "Process ForkProcess-20:\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "Process ForkProcess-32:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "Process ForkProcess-11:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "Process ForkProcess-22:\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 103, in get\n",
      "    res = self._recv_bytes()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/connection.py\", line 216, in recv_bytes\n",
      "    buf = self._recv_bytes(maxlength)\n",
      "Process ForkProcess-33:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/connection.py\", line 414, in _recv_bytes\n",
      "    buf = self._recv(4)\n",
      "Process ForkProcess-19:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/connection.py\", line 379, in _recv\n",
      "    chunk = read(handle, remaining)\n",
      "Process ForkProcess-16:\n",
      "KeyboardInterrupt\n",
      "Process ForkProcess-36:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "Process ForkProcess-30:\n",
      "Traceback (most recent call last):\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "Process ForkProcess-22:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "sampling loop time step:   7%|█              | 135/2000 [00:03<00:53, 34.74it/s]\n",
      "sampling loop time step:   7%|█              | 136/2000 [00:03<00:53, 34.90it/s]\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "Traceback (most recent call last):\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "/home/guyi/miniconda3/envs/llama/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py:54: UserWarning: Detected KeyboardInterrupt, attempting graceful shutdown...\n",
      "  rank_zero_warn(\"Detected KeyboardInterrupt, attempting graceful shutdown...\")\n",
      "KeyboardInterrupt\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 103, in get\n",
      "    res = self._recv_bytes()\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/connection.py\", line 216, in recv_bytes\n",
      "    buf = self._recv_bytes(maxlength)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/connection.py\", line 414, in _recv_bytes\n",
      "    buf = self._recv(4)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/connection.py\", line 379, in _recv\n",
      "    chunk = read(handle, remaining)\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "KeyboardInterrupt\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "Process ForkProcess-16:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/process.py\", line 108, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/concurrent/futures/process.py\", line 240, in _process_worker\n",
      "    call_item = call_queue.get(block=True)\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/queues.py\", line 102, in get\n",
      "    with self._rlock:\n",
      "  File \"/home/guyi/miniconda3/envs/llama/lib/python3.10/multiprocessing/synchronize.py\", line 95, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "KeyboardInterrupt\n"
     ]
    }
   ],
   "source": [
    "!python main.py fit -c ./conf/config.yaml"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "llama",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
